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Nowadays, many image processing and machine learning (ML) methods are used in mango-quality classification systems. Existing algorithms perform independently to capture the relationships between features in the dataset. Nevertheless, each method has its advantages and disadvantages. As a result, this study presents an ensemble-learning framework that combines the strengths of multiple ML algorithms to make predictions. Initially, different image processing algorithms are used to extract external mango features. Next, the dataset is constructed by combining those features with weight values from sensor signals. Following that, different ML algorithms are evaluated on the dataset to determine which ones are robust. Subsequently, various ensemble-learning approaches are deployed, such as bagging, boosting, and stacking. Finally, those models are evaluated and compared, to decide which model is suited for this study's dataset. In the experimental part, the assessment of errors demonstrates the usefulness of image processing algorithms. Furthermore, evaluation of the training models revealed that the stacking model, which integrates several methods in both the base learner and meta-learner, produced the highest results in precision, recall, F1-score, and accuracy, with values of 0.9855, 0.9901, 0.9876, and 0.9863, respectively. These experimental results confirm the robustness of the proposed methodologies for classifying mango quality. [ABSTRACT FROM AUTHOR]

Oil palm (Elaeis guineensis) is a widely cultivated crop known for its high oil yield. It is cultivated extensively across tropical regions, notably in Southeast Asia, Africa, and Latin America. It plays a vital role in global vegetable oil supply, meeting approximately 35% of the world's demand. However, the expansion of oil palm plantations often involves the utilization of degraded soils where nutrient availability, particularly nitrogen, is limited, posing challenges to plant growth and productivity. Roots are crucial in responding to nitrogen deficiency by adjusting their growth and distribution; however, research on root system distribution patterns in oil palm still needs to be completed. This study analyzes the root system architecture using RhizoVision Explorer, a 2D root image processing software while assessing its relationship with nitrogen availability across two commercial cultivars: Deli × La Mé of African oil palm (Elaeis guineensis) and the interspecific hybrid O×G Coari × La Mé. Our findings reveal significant associations between eight root traits in oil palm seedlings and treatments with and without nitrogen availability. Notably, total root morphology (length, surface area, and volume), rotation angle, solidity, and hole characteristics decreased under nitrogen deprivation, whereas surface angle frequency increased. We highlight the variability of these traits across cultivars, suggesting genetic dependence and potential utility in breeding programs. Moreover, interactions observed in primary root morphology and hole size indicate greater differences between control and nitrogen-treated groups in C × LM than in D × LM cultivars. On the other hand, cultivar differences, regardless of nitrogen availability, influenced lateral root morphology, while nitrogen availability, irrespective of cultivar, affected inclined angle frequency. Significant differences were observed in growth and development parameters such as root and shoot biomass, root-to-shoot ratio, and leaf emission numbers between nitrogen-optimal and nitrogen-starved conditions. Nitrogen significantly affects root architecture and plant growth in oil palm, particularly in the C × LM cultivar during the nursery stage. [ABSTRACT FROM AUTHOR]

This study was conducted on Xanthomonas axonopodis pv, which causes significant economic losses in the agricultural sector. Here, we study a common bacterial blight disease caused by the phaseoli (XaP) bacterial pathogen on Üstün42 and Akbulut bean genera. In this study, a total of 4000 images, healthy and diseased, were used for both bean breeds. These images were classified by AlexNet, VGG16, and VGG19 models. Later, reclassification was performed by applying pre-processing to the raw images. According to the results obtained, the accuracy rates of the pre-processed images classified by the VGG19, VGG16 and AlexNet models were determined as 0.9213, 0.9125 and 0.8950, respectively. The models were then hybridized with LSTM and BiLSTM for raw and pre-processed images and new models were created. When the performance of these hybrid models was evaluated, it was found that the models hybridized with LSTM were more successful than the simple models, while the models hybridized with BiLSTM gave better results than the models hybridized with LSTM. In particular, the VGG19+BiLSTM model attracted attention by achieving 94.25% classification accuracy with pre-processed images. This study emphasizes the effectiveness of image processing techniques in agriculture in the field of disease detection and is important as a new dataset in the literature for evaluating the performance of hybridized models. [ABSTRACT FROM AUTHOR]

The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection.

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, “smart” technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. “Smart Agriculture/farming” (SA) or “Digital Agriculture” (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA’s ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users’ facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language.

Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated. [ABSTRACT FROM AUTHOR]

This work was aimed at developing a prototype system based on multispectral digital photogrammetry to support tests required by international regulations for new Plant Protection Products (PPPs). In particular, the goal was to provide a system addressing the challenges of a new PPP evaluation with a higher degree of objectivity with respect to the current one, which relies on expert evaluations. The system uses Digital Photogrammetry, which is applied to multispectral acquisitions and Artificial Intelligence (AI). The goal of this paper is also to simplify the present screening process, moving it towards more objective and quantitative scores about phytotoxicity. The implementation of an opportunely trained AI model for phytotoxicity prediction aims to convert ordinary human visual observations, which are presently provided with a discrete scale (forbidding a variance analysis), into a continuous variable. The technical design addresses the need for a reduced dataset for training the AI model and relating discrete observations, as usually performed, to some proxy variables derived from the photogrammetric multispectral 3D model. To achieve this task, an appropriate photogrammetric multispectral system was designed. The system operates in multi-nadiral-view mode over a bench within a greenhouse exploiting an active system for lighting providing uniform and diffuse illumination. The whole system is intended to reduce the environmental variability of acquisitions tending to a standard situation. The methodology combines advanced image processing, image radiometric calibration, and machine learning techniques to predict the General Phytotoxicity percentage index (PHYGEN), a crucial measure of phytotoxicity. Results show that the system can generate reliable estimates of PHYGEN, compliant with existing accuracy standards (even from previous PPPs symptom severity models), using limited training datasets. The proposed solution addressing this challenge is the adoption of the Logistic Function with LASSO model regularization that has been shown to overcome the limitations of a small sample size (typical of new PPP trials). Additionally, it provides the estimate of a numerical continuous index (a percentage), which makes it possible to tackle the objectivity problem related to human visual evaluation that is presently based on an ordinal discrete scale. In our opinion, the proposed prototype system could have significant potential in improving the screening process for new PPPs. In fact, it works specifically for new PPPs screening and, despite this, it has an accuracy consistent with the one ordinarily accepted for human visual approaches. Additionally, it provides a higher degree of objectivity and repeatability. [ABSTRACT FROM AUTHOR]

This document, titled "Agricultural Unmanned Systems: Empowering Agriculture with Automation," emphasizes the importance of automation in modern agriculture. It discusses how automation improves production efficiency, reduces labor costs, addresses natural disasters, and promotes sustainability. The document explores the use of unmanned systems in smart agriculture, including drones, robots, and underwater vehicles. It also presents several studies that highlight the application of intelligent algorithms in precision agriculture, such as pest detection, crop water and nitrogen content identification, fruit and vegetable harvesting, and obstacle detection. The document concludes by acknowledging the need for further research and innovation in the areas of sky, air, and water in smart agriculture. [Extracted from the article]

Nowadays, many image processing and machine learning (ML) methods are used in mango-quality classification systems. Existing algorithms perform independently to capture the relationships between features in the dataset. Nevertheless, each method has its advantages and disadvantages. As a result, this study presents an ensemble-learning framework that combines the strengths of multiple ML algorithms to make predictions. Initially, different image processing algorithms are used to extract external mango features. Next, the dataset is constructed by combining those features with weight values from sensor signals. Following that, different ML algorithms are evaluated on the dataset to determine which ones are robust. Subsequently, various ensemble-learning approaches are deployed, such as bagging, boosting, and stacking. Finally, those models are evaluated and compared, to decide which model is suited for this study’s dataset. In the experimental part, the assessment of errors demonstrates the usefulness of image processing algorithms. Furthermore, evaluation of the training models revealed that the stacking model, which integrates several methods in both the base learner and meta-learner, produced the highest results in precision, recall, F1-score, and accuracy, with values of 0.9855, 0.9901, 0.9876, and 0.9863, respectively. These experimental results confirm the robustness of the proposed methodologies for classifying mango quality.

Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated.

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. [ABSTRACT FROM AUTHOR]

In Asia, rice is the most consumed grain by humans, serving as a staple food in India. The yield of rice paddies is easily affected by nutrient deficiencies and leaf diseases. To overcome this problem and improve the yield productivity of rice, nutrient deficiency and leaf disease identification are essential. The main nutrient elements in paddies are potassium, phosphorus, and nitrogen (PPN), the deficiency of any of which strongly affects the rice plants. When multiple nutrient elements are deficient, the leaf color of the rice plants is altered. To overcome this problem, optimal nutrient delivery is required. Hence, the present study proposes the use of Fuzzy C Means clustering (FCM) with Improved Tunicate Swarm Optimization (ITSO) to segment the lesions in rice plant leaves and identify the deficient nutrients. The proposed ITSO integrates the Tunicate Swarm Optimization (TSO) and Bacterial Foraging Optimization (BFO) approaches. The Hybrid Convolutional Neural Network (HCNN), a deep learning model, is used with ITSO to classify the rice leaf diseases, as well as nutrient deficiencies in the leaves. Two datasets, namely, a field work dataset and a Kaggle dataset, were used for the present study. The proposed HCNN-ITSO classified Bacterial Leaf Bright (BLB), Narrow Brown Leaf Spot (NBLS), Sheath Rot (SR), Brown Spot (BS), and Leaf Smut (LS) in the field work dataset. Furthermore, the potassium-, phosphorus-, and nitrogen-deficiency-presenting leaves were classified using the proposed HCNN-ITSO in the Kaggle dataset. The MATLAB platform was used for experimental analysis in the field work and Kaggle datasets in terms of various performance measures. When compared to previous methods, the proposed method achieved the best accuracies of 98.8% and 99.01% in the field work and Kaggle datasets, respectively. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of Chinese flowering cabbage ripeness and the counting of Chinese flowering cabbage are fundamental for timely harvesting, yield prediction, and field management. The complexity of the existing model structures somewhat hinders the application of recognition models in harvesting machines. Therefore, this paper proposes the lightweight Cabbage-YOLO model. First, the YOLOv8-n feature pyramid structure is adjusted to effectively utilize the target's spatial structure information as well as compress the model in size. Second, the RVB-EMA module is introduced as a necking optimization mechanism to mitigate the interference of shallow noise in the high-resolution sounding layer and at the same time to reduce the number of parameters in this model. In addition, the head uses an independently designed lightweight PCDetect detection head, which enhances the computational efficiency of the model. Subsequently, the neck utilizes a lightweight DySample upsampling operator to capture and preserve underlying semantic information. Finally, the attention mechanism SimAm is inserted before SPPF for an enhanced ability to capture foreground features. The improved Cabbage-YOLO is integrated with the Byte Tracker to track and count Chinese flowering cabbage in video sequences. The average detection accuracy of Cabbage-YOLO can reach 86.4%. Compared with the original model YOLOv8-n, its FLOPs, the its number of parameters, and the size of its weights are decreased by about 35.9%, 47.2%, and 45.2%, respectively, and its average detection precision is improved by 1.9% with an FPS of 107.8. In addition, the integrated Cabbage-YOLO with the Byte Tracker can also effectively track and count the detected objects. The Cabbage-YOLO model boasts higher accuracy, smaller size, and a clear advantage in lightweight deployment. Overall, the improved lightweight model can provide effective technical support for promoting intelligent management and harvesting decisions of Chinese flowering cabbage. [ABSTRACT FROM AUTHOR]

Sensors are very import parts of the IoT (Internet of Things). To detect the cleaning loss of combine harvesters during operation, a new detection method based on acoustic signal was proposed. The simulation models of the impact among grain, stem and plate were established by using the ALE algorithm, and the vibration excitation characteristics of the grain and stem were investigated; the results showed that both grain and stem signal were generated by surface vibration's excitation of the air, and the highest amplitudes of grain and stem signal were 15 kHz and 7 kHz respectively, which made it possible to distinguish grain and stem impact acoustic signals. Therefore, this paper proposed a novel method to separate the grain and stem signals by identifying the characteristic frequency area and subsequently an improved EMD denoising method based on an autocorrelation function was proposed. The critical stratum of EMD was identified based on the autocorrelation value of the eigenmode function, and the improved de-noising method was able to effectively remove the noise and the stem signal. To verify the feasibility of the improved denoising method, a field wheat harvesting experiment was conducted to acquire the actual acoustic signal of the cleaning operation. Data processing results showed that the average error of the comparison between the detected loss amount and the actual loss amount was 6.1%. This paper presents a novel approach for researching combine harvester cleaning loss detection and denoising methods. [ABSTRACT FROM AUTHOR]

This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than 5 × 10 4   L u x , the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone. [ABSTRACT FROM AUTHOR]

Chemical fungicides can effectively prevent and control powdery mildew, but they can also leave pesticide residues in the environment and on cucumbers. In this study, we added sulfur powder to the soil where cucumbers were grown to see how it affected the occurrence of powdery mildew. The results showed that adding sulfur increased sulfur absorption by the cucumbers, improved plant immunity, and reduced the incidence of powdery mildew. Furthermore, adding sulfur to the soil increased soluble protein content in cucumber leaves, enhanced photosynthesis, and significantly increased fruit yield. Additionally, sulfur addition decreased soil dehydrogenase activity and increased sucrase activity, potentially impacting soil microbial activity. In conclusion, this study found that adding sulfur had a positive inhibitory effect on the occurrence of cucumber powdery mildew while not significantly impacting the soil environment. These findings provide valuable insights for developing new control methods that are easy to implement, cost-effective, reliable, and environmentally safe. [ABSTRACT FROM AUTHOR]

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model's sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model's capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting. [ABSTRACT FROM AUTHOR]

Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of medicinal plants. In the study presented herein, we analyzed feature variables that affect the phenolic content of Korean Cnidium officinale Makino (C. officinale Makino) under different climate change scenarios. We applied different climate change scenarios based on environmental information obtained from Yeongju city, Gyeongsangbuk-do, Republic of Korea, and cultivated C. officinale Makino to collect data. The collected data included 3237, 75, and 45 records, and data augmentation was performed to address this data imbalance. We designed a function based on the DPPH value to set the phenolic content grade in C. officinale Makino and proposed a stacking ensemble model for predicting the total phenol contents and classifying the phenolic content grades. The regression model in the performance evaluation presented an improvement of 6.23–7.72% in terms of the MAPE; in comparison, the classification model demonstrated a 2.48–3.34% better performance in terms of accuracy. The classification accuracy was >0.825 when classifying phenol content grades using the predicted total phenol content values from the regression model, and the area under the curve values of the model indicated high model fitness (0.987–0.981). We plan to identify the key feature variables for the optimal cultivation of C. officinale Makino and explore the relationships among these feature variables. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field. [ABSTRACT FROM AUTHOR]

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young "Okubo" peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model's lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the "Okubo" peach fruit-thinning robot and scientific management of orchards. [ABSTRACT FROM AUTHOR]

Accurately recognizing apples in complex environments is essential for automating apple picking operations, particularly under challenging natural conditions such as cloudy, snowy, foggy, and rainy weather, as well as low-light situations. To overcome the challenges of reduced apple target detection accuracy due to branch occlusion, apple overlap, and variations between near and far field scales, we propose the Rep-ViG-Apple algorithm, an advanced version of the YOLO model. The Rep-ViG-Apple algorithm features a sophisticated architecture designed to enhance apple detection performance in difficult conditions. To improve feature extraction for occluded and overlapped apple targets, we developed the inverted residual multi-scale structural reparameterized feature extraction block (RepIRD Block) within the backbone network. We also integrated the sparse graph attention mechanism (SVGA) to capture global feature information, concentrate attention on apples, and reduce interference from complex environmental features. Moreover, we designed a feature extraction network with a CNN-GCN architecture, termed Rep-Vision-GCN. This network combines the local multi-scale feature extraction capabilities of a convolutional neural network (CNN) with the global modeling strengths of a graph convolutional network (GCN), enhancing the extraction of apple features. The RepConvsBlock module, embedded in the neck network, forms the Rep-FPN-PAN feature fusion network, which improves the recognition of apple targets across various scales, both near and far. Furthermore, we implemented a channel pruning algorithm based on LAMP scores to balance computational efficiency with model accuracy. Experimental results demonstrate that the Rep-ViG-Apple algorithm achieves precision, recall, and average accuracy of 92.5%, 85.0%, and 93.3%, respectively, marking improvements of 1.5%, 1.5%, and 2.0% over YOLOv8n. Additionally, the Rep-ViG-Apple model benefits from a 22% reduction in size, enhancing its efficiency and suitability for deployment in resource-constrained environments while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method's high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. [ABSTRACT FROM AUTHOR]

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of weeds is the prerequisite and foundation for precision weeding, automation, and intelligent field operations. Due to the wide variety of weeds in the field and their significant morphological differences, most existing detection methods can only recognize major crops and weeds, with a pressing need to enhance accuracy. This study introduces a novel weed detection approach that integrates the GFPN (Green Feature Pyramid Network), Slide Loss, and multi-SEAM (Spatial and Enhancement Attention Modules) to enhance accuracy and improve efficiency. This approach recognizes crop seedlings utilizing an improved YOLO v8 algorithm, followed by the reverse detection of weeds through graphics processing technology. The experimental results demonstrated that the improved YOLO v8 model achieved remarkable performance, with an accuracy of 92.9%, a recall rate of 87.0%, and an F1 score of 90%. The detection speed was approximately 22.47 ms per image. And when shooting from a height ranging from 80 cm to 100 cm in the field test, the crop detection effect was the best. This reverse weed detection method addresses the challenges posed by weed diversity and complexities in image recognition modeling, thereby contributing to the enhancement of automated and intelligent weeding efficiency and quality. It also provides valuable technical support for precision weeding in farmland operations. [ABSTRACT FROM AUTHOR]

The identification and enumeration of peach seedling fruits are pivotal in the realm of precision agriculture, greatly influencing both yield estimation and agronomic practices. This study introduces an innovative, lightweight YOLOv8 model for the automatic detection and quantification of peach seedling fruits, designated as YOLO-Peach, to bolster the scientific rigor and operational efficiency of orchard management. Traditional identification methods, which are labor-intensive and error-prone, have been superseded by this advancement. A comprehensive dataset was meticulously curated, capturing the rich characteristics and diversity of peach seedling fruits through high-resolution imagery at various times and locations, followed by meticulous preprocessing to ensure data quality. The YOLOv8s model underwent a series of lightweight optimizations, including the integration of MobileNetV3 as its backbone, the p2BiFPN architecture, spatial and channel reconstruction convolution, and coordinate attention mechanism, all of which have significantly bolstered the model's capability to detect small targets with precision. The YOLO-Peach model excels in detection accuracy, evidenced by a precision and recall of 0.979, along with an mAP50 of 0.993 and an mAP50-95 of 0.867, indicating its superior capability for peach sapling identification with efficient computational performance. The findings underscore the model's efficacy and practicality in the context of peach seedling fruit recognition. Ablation studies have shed light on the indispensable role of each component, with MobileNetV3 streamlining the model's complexity and computational load, while the p2BiFPN architecture, ScConv convolutions, and coordinate attention mechanism have collectively enhanced the model's feature extraction and detection precision for minute targets. The implications of this research are profound, offering a novel approach to peach seedling fruit recognition and serving as a blueprint for the identification of young fruits in other fruit species. This work holds significant theoretical and practical value, propelling forward the broader field of agricultural automation. [ABSTRACT FROM AUTHOR]

Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model's generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions. [ABSTRACT FROM AUTHOR]

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat. [ABSTRACT FROM AUTHOR]

The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB. [ABSTRACT FROM AUTHOR]

Excessive fertilizer use has led to environmental pollution and reduced crop yields, underscoring the importance of research into variable-rate fertilization (VRF) based on digital image technology in precision agriculture. Current methods, which rely on spectral sensors for monitoring and prescription mapping, face significant technical challenges, high costs, and operational complexities, limiting their widespread adoption. This study presents an automated, intelligent, and precise approach to maize canopy image segmentation using the multi-scale attention and Unet model to enhance VRF decision making, reduce fertilization costs, and improve accuracy. A dataset of maize canopy images under various lighting and growth conditions was collected and subjected to data augmentation and normalization preprocessing. The MCAC-Unet model, built upon the MobilenetV3 backbone network and integrating the convolutional block attention module (CBAM), atrous spatial pyramid pooling (ASPP) multi-scale feature fusion, and content-aware reassembly of features (CARAFE) adaptive upsampling modules, achieved a mean intersection over union (mIOU) of 87.51% and a mean pixel accuracy (mPA) of 93.85% in maize canopy image segmentation. Coverage measurements at a height of 1.1 m indicated a relative error ranging from 3.12% to 6.82%, averaging 4.43%, with a determination coefficient of 0.911, meeting practical requirements. The proposed model and measurement system effectively address the challenges in maize canopy segmentation and coverage assessment, providing robust support for crop monitoring and VRF decision making in complex environments. [ABSTRACT FROM AUTHOR]

Differences in land use type and chronological age affect soil properties and plant community characteristics, which may influence soil structural stability and erodibility. However, knowledge on the effects of soil physicochemical properties on soil aggregate stability and erodibility at different land use years is limited. This study selected five land use types: corn field (Year 38th-y), corn intercropped with cabbage field (Year 38th-y + b), fruit and meridian forest (Year 6th-jgl), naturally restored vegetation (Year 6th-zr), and artificial forest (Year 7th-rgl) in the karst landscape of the Chishui River Basin in Yunnan Province. We aimed to identify the influencing factors of soil stability and erodibility under different land use time series. The results indicated that the mean weight diameter (MWD), the geometric mean diameter (GMD), and soil structural stability index (SSI values) were highest in Y6th-zr and lowest in Y7th-rgl. Conversely, the erodibility K value was lowest in Y6th-zr, suggesting that the soil structure in Y6th-zr exhibited greater stability, whereas soil stability in Y7th-rgl was lower. Redundancy and throughput analyses revealed that organic carbon and water-stable aggregates > 2.0 mm content had higher vector values. Soil bulk density, total nitrogen, organic carbon, and soil texture content were the main factors contributing to soil stability variation (0.338–0.646). Additionally, total nitrogen, organic carbon, total phosphorus, and soil texture content drove the variation in K values (0.15–1.311). Natural vegetation restoration measures can enhance soil structure to a certain extent. These findings highlight changes in soil aggregate stability and erodibility over different land use durations. The research results have important theoretical and practical significance for understanding the differences in soil erosion and soil restoration under different land use patterns in the karst landscapes of southwest China. [ABSTRACT FROM AUTHOR]

The lowest height at which a product can fall without suffering severe harm is known as the "critical drop height" for agricultural products. It is a crucial factor to take into account for crops like loquats that are prone to bruising or damage upon impact. By establishing the minimum altitude at which the product can be dropped without experiencing substantial harm, suitable processing procedures may be established from harvest to the end consumer, thereby preserving product quality and worth. The critical drop height can be ascertained through swift, affordable, non-destructive, and non-traditional methods, rather than time-consuming and expensive laboratory trials. In the study, we aimed to estimate the critical drop height for loquat fruit using machine learning methods. Three different machine learning methods with different operating principles were applied. R2, MAE, RMSE, and MAPE metrics were used to assess the models. There were no obvious differences in both the comparisons within the models, namely the training and test results and the mutual comparisons of the models. However, with a slight difference, the SVMs model performed better in the training data set, and the ETs model performed better in the test data set. Plots were drawn to visualize model performances, and the results obtained from the plots and metrics support each other. [ABSTRACT FROM AUTHOR]

Accurate detection of early diseased plants is of great significance for high quality and high yield of crops, as well as cultivation management. Aiming at the low accuracy of the traditional deep learning model for disease diagnosis, a crop disease recognition method was proposed based on multi-source image fusion. In this study, the adzuki bean rust disease was taken as an example. First, color and thermal infrared images of healthy and diseased plants were collected, and the dynamic thresholding excess green index algorithm was applied to extract the color image of the canopy as the reference image, and the affine transformation was used to extract the thermal infrared image of the canopy. Then, the color image was fused with the thermal infrared image by using a linear weighting algorithm to constitute a multi-source fusion image. In addition, the sample was randomly divided into a training set, validation set, and test set according to the ratio of 7:2:1. Finally, the recognition model of adzuki bean rust disease was established based on a novel deep learning model (ResNet-ViT, RMT) combined with the improved attention mechanism and the Squeeze-Excitation channel attention mechanism. The results showed that the average recognition rate was 99.63%, the Macro-F1 was 99.67%, and the recognition time was 0.072 s. The research results realized the efficient and rapid recognition of adzuki bean rust and provided the theoretical basis and technical support for the disease diagnosis of crops and the effective field management. [ABSTRACT FROM AUTHOR]

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer's channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management. [ABSTRACT FROM AUTHOR]

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film's semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing. [ABSTRACT FROM AUTHOR]

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology. [ABSTRACT FROM AUTHOR]

In the context of sustainability, the integration of renewable energy into industrial processes not only minimizes dependence on fossil fuels but also contributes to the efficient management of industrial waste. By transforming organic waste, including agri-food and urban waste, into biogas, green energy can be generated, thus reducing the impact on the environment and closing the loop of material used in the economic circuit. Thus, a sustainable system can be promoted, where resources are continuously reused and exploited. Statistical methods and a decision tree with the Classification and Regression Trees (CRT) algorithm were employed to analyze data. The paper focuses on the importance of industrial waste and biogas for the generation, transformation, and consumption of energy in the EU (European Union)-27 countries. To provide a thorough analysis, we have divided these countries based on real gross domestic product (GDP) per capita, grouping them above/below the annual average for the period 2012–2021/2022. Descriptive statistics revealed observable differences between the two groups, but the paper aimed to provide evidence regarding the existence of these differences as statistically significant. Using the Kolmogorov–Smirnov test, the non-normal distribution of the data was confirmed, requiring non-parametric inferential methods. The Mann–Whitney U test revealed statistically significant differences between the two groups for all the studied variables. This comprehensive approach highlights the distinct energy-related characteristics influenced by economic development in the EU-27. [ABSTRACT FROM AUTHOR]

Rice plants' ability to develop lodging resistance is essential for their proper growth and development, and understanding the stress–strain relationship is crucial for a comprehensive analysis of this resilience. Nevertheless, significant data variability, inefficiency, and substantial observational inaccuracies hinder current measurement and analysis techniques. Therefore, this study proposes a machine vision-based stress–strain measurement method for rice plants to address these limitations. The technique primarily involves the implementation of the proposed MV-SSRP rotating target detection network, which enhances the model's ability to predict the strain of rice stalks accurately when subjected to bending forces through the integration of the spatial channel reorganization convolution (ScConv) and Squeeze-and-Excitation (SE) attention mechanism. A stress–strain dynamic relationship model was also developed by incorporating real-time stress data obtained from a mechanical testing device. The experimental findings demonstrated that MV-SSRP attained precision, recall, and mean average precision (mAP) rates of 93.4%, 92.6%, and 97.6%, respectively, in the context of target detection. These metrics represented improvements of 4.8%, 3.8%, and 5.1%, respectively, over the performance of the YOLOv8sOBB model. This investigation contributes a theoretical framework and technical underpinning for examining rice lodging resistance. [ABSTRACT FROM AUTHOR]

To demonstrate the feasibility and improve the implementation of laser weeding, a static movable lift-adjustable closed fiber laser weeding device and system have been developed, followed by experiments and performance evaluations. Physical experiments were conducted on the energy required for laser cutting of weed stems, targeting four common larger weeds (Chenopodium album, Amaranthus spinosus, Setaria viridis, and Eleusine indica) in farmland and orchards. At the same irradiation distances, the energy required to cut the same type of large weed generally increases with increasing distances and stem diameters but decreases with increasing irradiation time. The variance of stems' power density after irradiation was larger and the values were more dispersed for Chenopodium album and Setaria viridis weeds, and the values were relatively scattered, while the power density values of Amaranthus spinosus and Eleusine indica weeds were more concentrated. When the irradiation time was 10 s, the 3.892 W/mm2 laser was sufficient to eliminate weeds and plants with the irradiation distances of 2 m. The 2.47 W/mm2 laser was more effective, as it killed weeds within a distance of 1 m in less than 1 s. This work demonstrates the feasibility of the laser weeding device and system that can completely cut off the stems of large weeds, and this technology has the potential to promote sustainable agriculture. [ABSTRACT FROM AUTHOR]

Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement. [ABSTRACT FROM AUTHOR]

Large quantities of pumpkins, rich in valuable nutrients, are lost due to superficial imperfections or size variations. This study explores a solution: transforming this unused resource into a highly functional food ingredient-pumpkin powder obtained from dehydration. This study emphasizes the importance of a detailed particle-level mathematical model in dehydrator design and operation, particularly for drying conditions using air at temperatures between 333 K and 353 K. The model investigates the effect of sample geometry on the moisture reduction rate and the product quality. Here, a model considers mass and energy transport, including the shrinkage ratio of the samples. The results effectively demonstrate the deformation, moisture content, and temperature evolution within the samples throughout the drying process. The findings reveal that both the drying temperature and initial sample geometry significantly influence the moisture loss rate, the final product texture, and the powder's absorption capacity. Notably, the nutritional composition (except for lipids) remains largely unaffected by the drying process. Additionally, the bulk and compacted densities of the powders decrease with increasing temperature. These insights not only illuminate the performance of the drying process but also provide valuable knowledge regarding the dehydrated product's technological behavior and potential functionalities within various food applications. [ABSTRACT FROM AUTHOR]

Conservation tillage, a crucial method for protecting soil fertility, depends heavily on maintaining adequate straw coverage. The current method of straw coverage detection relies primarily on manual measurement, which is both time-consuming and laborious. This paper introduces a novel straw coverage detection approach based on an improved mask regional convolutional neural network (Mask R-CNN) algorithm. Several images of wheat straw-covered fields were taken, and the dataset was augmented using techniques like image inversion, contrast enhancement, Gaussian noise addition, and translation after cropping the original images. These fields use a crop rotation cycle of wheat and corn. Subsequently, the straw images were annotated using the Labelme annotation tool to obtain the available straw instance segmentation dataset. The Mask R-CNN algorithm was improved by refining the mask generation network structure through a multi-feature fusion strategy, which interweaves features from both the encoder and the mask generation network, enhancing the model's ability to capture detailed and shape information of the straw. Lastly, using the mask information output by the improved Mask R-CNN algorithm, the straw coverage was calculated by counting the proportion of pixels within each segmented region. In the results, compared to the original Mask R-CNN algorithm, our improved Mask R-CNN algorithm achieved an average improvement of 7.8% in segmentation accuracy, indicating that the improved Mask R-CNN algorithm offers superior segmentation performance. Thus, the new algorithm can achieve straw coverage detection with higher accuracy and can provide a reference for other agricultural applications. [ABSTRACT FROM AUTHOR]

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs. [ABSTRACT FROM AUTHOR]

Different types of soil macropores respond differently to various tillage practices, owing to disparities in origin, scale, morphology, and function, consequently exerting distinct effects on soil structure. This study aimed to investigate the response mechanisms of three different soil pore types (total macropores, non-biopores, and biopores) to two distinct tillage practices: smash-ridging tillage (T) and no-tillage (NT) in sugarcane fields. The parameters characterizing soil pore treatments in two and three dimensions were obtained using X-ray computed tomography scanning technology. ImageJ and MATLAB software were employed to analyze the data and separate soil macropores into biopores and non-biopores categories. The results showed that non-biopores predominated in two-dimensional cross-sectional areas in NT treatment, whereas biopores were more dominant in T treatment. Biopores in T treatment had a higher proportion of two-dimensional pores compared to NT treatment. A three-dimensional analysis indicated that total macropores had larger mean diameters (MD) and macroporosity, with more continuous tubular pores in T treatment than that in NT treatment. However, NT treatment had more numerous non-biopores with broader spatial distribution and complex morphology. Additionally, biopores in T treatment had larger MD and branching length density (LD). These vertically developed biopores, along with high macropore connectivity and under smash-ridging tillage, could improve soil water and pore conductivity. Therefore, smash-ridging tillage was more beneficial for sugarcane growth compared to no-tillage in Guangxi of China. [ABSTRACT FROM AUTHOR]

We evaluated the effects of fungicides on strawberry powdery mildew (PM) fungus, Podosphaera aphanis, using an electrostatic technique. Thirty-six fungicides were sprayed on single colonies of P. aphanis on leaves of strawberry seedlings (Fragaria × ananassa Duchesne ex Rozier). Colony development varied depending on the tested fungicides. Particularly, pyraziflumid, triflumizole, triforine, polyoxin, sodium hydrogen carbonate + copper wettable powder, and flutianil + mepanipyrim were highly effective for reducing colony development. P. aphanis colonies were histochemically stained to observe the morphological characteristics of fungal cells forming normal and abnormal conidiophores. Abnormal conidiophores were classified into seven types based on their morphological and cytological characteristics. Finally, asexual conidia were collected from single P. aphanis colonies on the leaves spray-treated with fungicides using a dielectrically polarized insulator plate (electrostatic spore collector); conidia attracted to the insulator plates were counted using a high-fidelity digital microscope. Most tested fungicides highly inhibited the production and/or germination of asexual conidia. The germination of asexual conidia was observed only in thiophanate-methyl (methyl benzimidazole carbamates fungicides; MBC fungicides) and azoxystrobin (quinone outside inhibitors; QoI fungicides). Assessing with the electrostatic technique, we clarified that P. aphanis has developed resistance to both thiophanate-methyl and azoxystrobin. Thus, the methodological assessment analyzing the colony development and the number of conidia released from single colonies will be helpful information for screening effective fungicides. [ABSTRACT FROM AUTHOR]

Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information. This characteristic information includes both simple and intuitive features, as well as deeper abstract meanings. These complex features pose significant challenges to robots in determining fruit ripeness. To increase the precision, accuracy, and efficiency of robotic fruit maturity detection methods, a strawberry maturity detection algorithm based on an improved CES-YOLOv8 network structure from YOLOv8 was developed in this study. Initially, to reflect the characteristics of actual planting environments, the study collected image data under various lighting conditions, degrees of occlusion, and angles during the data collection phase. Subsequently, parts of the C2f module in the YOLOv8 model's backbone were replaced with the ConvNeXt V2 module to enhance the capture of features in strawberries of varying ripeness, and the ECA attention mechanism was introduced to further improve feature representation capability. Finally, the angle compensation and distance compensation of the SIoU loss function were employed to enhance the IoU, enabling the rapid localization of the model's prediction boxes. The experimental results show that the improved CES-YOLOv8 model achieves an accuracy, recall rate, mAP50, and F1 score of 88.20%, 89.80%, 92.10%, and 88.99%, respectively, in complex environments, indicating improvements of 4.8%, 2.9%, 2.05%, and 3.88%, respectively, over those of the original YOLOv8 network. This algorithm provides technical support for automated harvesting robots to achieve efficient and precise automated harvesting. Additionally, the algorithm is adaptable and can be extended to other fruit crops. [ABSTRACT FROM AUTHOR]

Mushroom cap is a key trait in the growth process and its phenotypic parameters are essential for automatic cultivation and smart breeding. However, the edible mushrooms are usually grown densely with mutual occlusion, which is difficult to obtain the phenotypic parameters non-destructively. Although deep learning methods achieve impressive performance with superior generalization capabilities, they require a large amount of ground truth label of the occluded target, which is a challenging task due to the substantial workload. To overcome this obstacle, a novel synthetic cap occlusion image method was proposed for rapidly generating edible mushroom occlusion datasets using raw images, in which the ground truth is obtained from the real world and the occlusion is randomly generated for simulating real scenes. Moreover, variants of amodal instance segmentation models with different backbone were trained and evaluated on our synthetic occlusion image datasets. Finally, an amodal mask-based size estimation method was presented to calculate the width and length of the cap. The experimental results showed that the amodal instance segmentation achieved an AP@[0.5:0.95] of 82%, 93% and 96% on Oudemansiella raphanipes, Agrocybe cylindraceas and Pholiota nameko synthetic cap datasets, respectively, with a size of 1024 × 1024 px, which indicates that our occlusion image synthesis method can effectively simulate the real cap occlusion situation. The size estimation method achieved an R 2 of 0.95 and 0.98 between predictive amodal caps and manually labeled caps for the length and width of Agrocybe cylindraceas cap, respectively, which can be applied to obtain the phenotypic parameters of each cap effectively and accurately. These methods not only meet the demand for automatic monitoring of edible mushroom morphology in factories but also provide technical support for intelligent breeding. [ABSTRACT FROM AUTHOR]

This paper proposes PestNet, a lightweight method for classifying crop pests, which improves upon MobileNet-V2 to address the high model complexity and low classification accuracy commonly found in pest classification research. Firstly, the training phase employs the AdamW optimizer and mixup data augmentation techniques to enhance the model's convergence and generalization capabilities. Secondly, the Adaptive Spatial Group-Wise Enhanced (ASGE) attention mechanism is introduced and integrated into the inverted residual blocks of the MobileNet-V2 model, boosting the model's ability to extract both local and global pest information. Additionally, a dual-branch feature fusion module is developed using convolutional kernels of varying sizes to enhance classification performance for pests of different scales under real-world conditions. Lastly, the model's activation function and overall architecture are optimized to reduce complexity. Experimental results on a proprietary pest dataset show that PestNet achieves classification accuracy and an F1 score of 87.62% and 86.90%, respectively, marking improvements of 4.20 percentage points and 5.86 percentage points over the baseline model. Moreover, PestNet's parameter count and floating-point operations are reduced by 14.10% and 37.50%, respectively, compared to the baseline model. When compared with ResNet-50, MobileNet V3-Large, and EfficientNet-B1, PestNet offers superior parameter efficiency and floating-point operation requirements, as well as improved pest classification accuracy. [ABSTRACT FROM AUTHOR]

To mitigate problems concerning small-sized spots on apple leaves and the difficulties associated with the accurate detection of spot targets exacerbated by the complex backgrounds of orchards, this research used alternaria leaf spots, rust, brown spots, gray spots, and frog eye leaf spots on apple leaves as the research object and proposed the use of a high-accuracy detection model YOLOv5-Res (YOLOv5-Resblock) and lightweight detection model YOLOv5-Res4 (YOLOv5-Resblock-C4). Firstly, a multiscale feature extraction module, ResBlock (residual block), was designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 (CSP Bottleneck with four convolutions) was designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter-streamlining strategy based on an optimized model architecture was proposed. The experimental results show that the performance of the YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, with the mAP0.5 values increasing by 2.8% and 2.2% compared to the YOLOv5s model and YOLOv5n model, respectively. The sizes of the YOLOv5-Res model and YOLOv5-Res4 model are only 10.8 MB and 2.4 MB, and the model parameter counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model. [ABSTRACT FROM AUTHOR]

Lotus seedpod maturity detection and segmentation in pond environments play a significant role in yield prediction and picking pose estimation for lotus seedpods. However, it is a great challenge to accurately detect and segment lotus seedpods due to insignificant phenotypic differences between the adjacent maturity, changing illumination, overlap, and occlusion of lotus seedpods. The existing research pays attention to lotus seedpod detection while ignoring maturity detection and segmentation problems. Therefore, a semantic segmentation dataset of lotus seedpods was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. Afterwards, an improved YOLOv8-seg model was proposed to detect and segment the maturity of lotus seedpods. In the model, the convolutional block attention module (CBAM) was embedded in the neck network to extract distinguished features of different maturity stages with negligible computation cost. Wise-Intersection over Union (WIoU) regression loss function was adopted to refine the regression inference bias and improve the bounding box prediction accuracy. The experimental results showed that the proposed YOLOv8-seg model provides an effective method for "ripe" and "overripe" lotus seedpod detection and instance segmentation, where the mean average precision of segmentation mask ( m A P m a s k ) reaches 97.4% and 98.6%, respectively. In addition, the improved YOLOv8-seg exhibits high robustness and adaptability to complex illumination in a challenging environment. Comparative experiments were conducted using the proposed YOLOv8-seg and other state-of-the-art instance segmentation methods. The results showed that the improved model is superior to the Mask R-CNN and YOLACT models, with recall, precision, m A P b o x and m A P m a s k being 96.5%, 94.3%, 97.8%, and 98%, respectively. The average running time and weight size of the proposed model are 25.9 ms and 7.4 M, respectively. The proposed model obtained the highest mAP for lotus seedpod maturity detection and segmentation while maintaining an appropriate model size and speed. Furthermore, based on the obtained segmentation model, 3D visualization of the lotus pond scene is performed, and cloud point of lotus seedpods is generated, which provides a theoretical foundation for robot harvesting in the lotus pond. [ABSTRACT FROM AUTHOR]

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model's feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model's detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management. [ABSTRACT FROM AUTHOR]