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Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge. This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods. The images are taken from an altitude of approximately 13 ft, with angles between 53 and 58 degrees, under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code and the labeled soybean dataset, offering a valuable resource for future research endeavors in soybean pod counting and related fields. • Created and open-sourced the 1st UAV-acquired image dataset for soybean pod counting. • Pioneered the first study on precise soybean pod counting utilizing UAV images. • Demonstrated SoybeanNet's superiority over 5 state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

• We provide a UAV-based multispectral imagery approach objectifying FHM strategies. • A CNN was trained using 17,643 images of trees with health status and species labels. • Analysis was done at 235 plots distributed across Bavaria over three years. • The highly heterogenous dataset benefits model generalization and transferability. • We provide a pipeline to generate reference data and for DL image classification. Uncrewed Aerial Vehicles (UAVs) have emerged as a promising tool for complementing terrestrial surveys, offering unique advantages for forest health monitoring (FHM). UAVs have the potential to improve or even replace core tasks such as crown condition assessment, bridging the gap between ground-based surveys and traditional remote sensing platforms. However, present approaches have not yet fully exploited the very high temporal resolution and flexible and convenient utilization that UAVs offer even under cloudy skies. In this paper, we provide a standardized data pipeline to semi-automatically generate reference data and for monitoring forest health by merging ground-based and UAV-based data related to species-specific forest health. Furthermore, we investigated the potential of Convolutional Neural Networks (CNNs) to classify the main tree species and their crown conditions based on the reference data. Therefore, we acquired high resolution multispectral drone imagery of 235 different ICP large scale forest monitoring plots (Level-I plots) distributed across Bavaria for three consecutive years (2020–2022). Using this highly heterogeneous time-series dataset, encompassing diverse weather and lighting conditions, forest stand characteristics, and spatial distribution of study areas, we successfully classified five tree species, three genus level classes and dead trees, including the health status of the main tree species occurring in Germany. This way we managed to classify 14 distinct classes with an average macro F1-score of 0.61 using the EfficientNet CNN architecture. The highest class-specific F1-score apart from the class of dead trees (0.97) was achieved by the class of Picea abies healthy (0.80). If participating countries of the ICP Forests program adopt our approach to harmonize terrestrial and UAV-based monitoring, many ground-based tasks could be reduced or replaced, leading to significant time and cost savings. We provide standardized and open-source monitoring and analysis strategies that can be potentially extended throughout Europe. Our findings demonstrate that UAV monitoring and deep learning can modernize forest management for efficiency and sustainability. We recommend integrating drones with ground surveys in forest monitoring systems to take advantage of their benefits. [ABSTRACT FROM AUTHOR]

• LodgeNet was used to obtain the information of different lodging degrees in rice. • LodgeNet achieved 97.30% accuracy in rice lodging semantic segmentation. • LodgeNet has a better effect than other networks on a small data set. Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Therefore, it is very important to identify rice lodging efficiently. For unmanned aerial vehicle (UAV) remote sensing images, this paper combines the advantages of dense block, DenseNet, attention mechanism, and jump connection on the basis of U-Net network to propose an end-to-end, pixel-to-pixel semantic segmentation method to identify rice lodging. And the method can process the input multi-band image. The accuracy of the model proposed in this paper was 97.30% on rice lodging images, which performed better than other comparison methods in the test. At the same time, it has good effect on small sample data set. The results show that it is feasible to use the improved U-Net network model to extract the lodging area of rice, which provide a useful reference for rice breeding and agricultural insurance claims. [ABSTRACT FROM AUTHOR]

• The method of semantic segmentation can effectively identify weed areas. • An improved weed identification model was proposed based on attention mechanism. • A semi-supervised semantic segmentation method is proposed. • The improved model can recognize more scattered and smaller groups of weed pixels than the original network. Based on Unmanned aerial vehicle (UAV) platforms, targeted weed management has become the mainstream method to address the harm caused by weeds in farmland, and accurate weed area identification is an important prerequisite for this approach. In this paper, we conducted research on the existing issues in weed recognition based on PSPNet. To enhance the feature extraction ability of the model and improve its performance in complex field environments, we explored the effects of three types of attention modules when inserted into different positions in the network. The experimental results showed that the best performance was achieved when the ECA module was inserted into the SPP layer of the network. Additionally, to improve the utilization of limited data and enhance model performance, we designed a semi-supervised semantic segmentation method and conducted ablation experiments to determine the optimal network parameters and verify their effectiveness. Finally, we combined these two comprehensive optimization strategies and tested them on a weed dataset. The results show that the mean pixel accuracy (MPA) and F-score of the improved weed identification model are 88.66 % and 87.79 %, 3.82 % and 2.74 % higher than that of the original network while effectively increasing prediction accuracy. This study can provide a practical reference for precise weed distribution mapping and subsequent implementation of adaptive spraying on UAV platforms. [ABSTRACT FROM AUTHOR]

• A detection approach for late-autumn shoots of litchi tree based on UAV remote sensing is provided. • A remote sensing dataset of late-autumn shoots of litchi tree is constructed. • A deep learning model based on the combination of YOLOv5 and Swin-transformer is proposed. • The detection accuracy of late-autumn shoots in remote sensing images is improved. • It will provide technical support for other researchers on late-autumn shoots and orchards manager. Litchi is one of the most common economic fruits in southern China, however, the growth of late-autumn shoots of litchi hinders flower bud differentiation and reduces yield of fruit. The early identification of the late-autumn shoots is of great significance for orchard management to control shoots and then increase fruit yield. At present, the identification of late-autumn shoots still relies on manual methods, which is not suitable for smart orchard management in a large area due to low recognition efficiency and high subjectivity. Therefore, a convenient, fast and cost-effective method is urgently needed. In response to this problem, the paper proposes a method based on the combination of unmanned aerial vehicle (UAV) remote sensing and object detection algorithm to detect late-autumn shoots. For this purpose, a remote sensing dataset of late-autumn shoots of litchi is first constructed by UAV. An improved YOLOv5 algorithm called YOLOv5-SBiC is then developed for late-autumn shoots identification. In the YOLOv5-SBiC algorithm, the transformer module is introduced to speed up the convergence of the network and improve detection accuracy, the attention mechanism module is employed to help the model extracting details, and BiFPN is used to better solve the multi-scale problem in detecting and then improve the recognition effect of small-sized objects. In addition, CIOU is selected as the loss function of bounding boxes regression to achieve high-precision localization of the boxes. The test results demonstrate that the recognition accuracy of YOLOv5-SBiC reaches a relatively high value of 79.6%, which is 4.0% higher than that (75.6%) of the original YOLOv5 algorithm and 15.9% higher than that (63.7%) of the pure transformer algorithm. It's also demonstrated that YOLOv5-SBiC is more competitive than the mainstream target detection algorithms in the dataset of late-autumn shoots. [ABSTRACT FROM AUTHOR]

Volunteer cotton (VC) plants growing in the fields of inter-seasonal and rotated crops, like corn, can serve as hosts to boll weevil pests once they reach pin-head square stage (5–6 leaf stage). The VC plants therefore need to be detected, located, and destroyed or sprayed. In this paper, we present a study on using deep learning (DL) to detect VC plants in a corn field using RGB images collected with an unmanned aerial vehicle (UAV). The objectives were (i) to determine whether the YOLOv3 DL algorithm could be used for VC detection in a corn field based on UAV-derived RGB images, and (ii) to investigate the behavior of YOLOv3 on images at three different pixel scales (320 × 320, S1; 416 × 416, S2; and 512 × 512, S3). The metrics used to evaluate the results were average precision (AP), mean average precision (mAP) and F1-score at 95 % confidence level. It was found that YOLOv3 was able to detect VC plants in corn field at an average detection accuracy of more than 80 %, F1-score of 78.5 % and mAP of 80.38 %. With respect to images size, no significant differences existed for mAP among the three scales, but a significant difference was found for AP between S1 and S3 (p = 0.04) and between S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The overall goal of this study was to minimize boll weevil pest infestation by maximizing the true positive detection of VC plants in a corn field which is represented by the mAP values. The lack of significant differences of these at all three scales indicated that the trained YOLOv3 model can be used for VC detection irrespective of the three input image sizes. The capability of YOLOv3 to detect VC plants demonstrates the potential of DL algorithms for real-time detection and mitigation using computer vision and a spot-spray capable UAV. [ABSTRACT FROM AUTHOR]

• An RGB-D camera is mounted on the UAV to collect RGB and depth images. • Combine object detection with semantic segmentation. • A method combining CNN and RGB-D image information. • The 3D localization of branch picking points based on deep learning. • The performance of the whole scheme was evaluated by orchard experiments. Longan is a famous speciality fruit and cultivated medicinal plant that has important edible and medicinal value; how to improve productivity in harvest is an important issue. At present, longan is mainly planted in hilly areas. For complex site conditions and tall trees, the ground harvesting machineries cannot work normally. In this study, aiming at harvesting longan fruit using unmanned aerial vehicles, a method combining an improved YOLOv5s, improved DeepLabv3+ model and depth image information is proposed, which is used for the three-dimensional (3D) positioning of branch picking points in complex natural environments. First, the improved YOLOv5s model is used to quickly detect longan fruit skewers and the main fruit branches from a complex orchard environment. The correct main fruit branch is obtained according to its relative position relationship and is extracted as the input to the semantic segmentation model. Second, using the improved DeepLabv3+ model, the image extracted in the previous step is semantically segmented to obtain the 2D coordinate information of the main longan fruit branches. Finally, combined with the growth characteristics of a longan fruit string, RGB-D information fusion is carried out on the main fruit branches in 3D space to obtain the central axis and pose information of the main fruit branches, and the 3D coordinates of the picking points are calculated, which provides destination information for a longan harvesting drone. To verify the effectiveness of the proposed method, an experiment for identifying and locating the main fruit branches and picking points was carried out in a longan orchard. The experimental results show that the longan string fruit and main fruit branch detection accuracy is 85.50%, and the main fruit branch semantic segmentation accuracy is 94.52%. The whole algorithm takes 0.58 s in the actual scene and can quickly and accurately locate the picking points. In summary, this paper fully exploits the advantages of the combination of a convolutional neural network and RGB-D image information, further improving the efficiency of longan harvesting drones in accurately positioning picking points in 3D space. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plant stand count methods using unmanned aerial vehicle imagery were reviewed. • Computer vision methods produced promising results in plant stand count analysis. • Several research gaps were identified and recommendations provided. • Results will be helpful to stakeholders like farmers, producers, and researchers. Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count. [ABSTRACT FROM AUTHOR]

• A deep learning method for oriented and small wheat spike detection was proposed to accurately detect wheat spikes in UAV images. • The method introduces the orientation feature of wheat spikes into the network and reduces the negative effect of background on detection based on circular smooth label. • The method can better solve tiny wheat spikes' occlusion and overlap problems. Detecting and characterizing spikes from wheat field images is essential in wheat growth monitoring for precision farming. Along with various technological developments, deep-learning-based methods have remarkably improved wheat spike detection performance. However, detecting small and overlapping wheat spikes in UAV images is still challenging because high spike occlusion and complex background can cause error detection and miss detection problems. This paper proposes a deep learning method for oriented and small wheat spike detection (OSWSDet). Unlike classical wheat spike detection methods, OSWSDet introduces the orientation of wheat spikes into the YOLO framework by integrating a circle smooth label (CSL) and a micro-scale detection layer. These improvements enhance the ability to detect small-sized wheat spikes and prevent wheat spike detection errors. The experiment results show that OSWSDet outperforms classical wheat spike detection methods, and the average accuracy (AP) is 90.5%. OSWSDet can accurately detect spikes in UAV images with complex field backgrounds and provides technical references for future field wheat phenotype monitoring. [ABSTRACT FROM AUTHOR]

• Various machine learning techniques were compared based on precision, recall, and training time. • Convolutional neural network model was developed to characterize soybean leaf defoliation. • Different configurations of neural network models were discussed regrading efficacy. • Different training methods can be used to favor precision or recall. Severe crop defoliation caused by insects and pests is linked to low agricultural productivity. If the root cause is not addressed, severe defoliation spreads, damaging whole crop fields. Understanding which areas are afflicted by severe defoliation can help farmers manage crops. Unmanned Aerial Vehicles (UAV) can fly over whole crop fields capturing detailed images. However, it is hard to characterize crop defoliation from aerial images that include multiple, overlapping plants with confounding effects from shadows and lighting. This paper assesses the efficacy of machine learning techniques to characterize defoliation. Given an UAV image as input, these techniques detect if severe defoliation is present. We created a labeled data set on soybean defoliation that comprises over 97,000 UAV images. We compared machine learning techniques ranging from Naive Bayes to neural networks and assessed their efficacy for (1) correctly characterizing images that contain defoliated crops and (2) avoiding wrong characterizations of healthy crops as defoliated. None of the techniques studied achieved high efficacy on both questions. However, we created DefoNet , a convolutional neural network designed for detecting crop defoliation that produces models that can be efficacious for either question. If adopted in practice, DefoNet models can guide decision making for mitigating crop yield losses due to defoliating insects. [ABSTRACT FROM AUTHOR]

• Detect and locate fruits based on deep learning is the key to the success of fruit picking. • UAV fuzzy image preprocessing is the basic condition for longan detection. • Reduce the number of calculations and parameters is vital for target detection task. • The positioning strategy of suitable cutting points on fruit branches is elaborated in detail. • The performance of picking platform is evaluated by performing picking experiments. In agriculture, fruit picking robots on the ground have difficulty adapting to the terrain conditions of mountain orchards and cannot pick longan fruit from tall longan trees. In this paper, aiming to allow picking of longan fruit by unmanned aerial vehicles (UAVs), a deep learning-based scheme to quickly and accurately detect and locate suitable picking points on fruit branches is proposed. The scheme includes a UAV fuzzy image preprocessing method, longan detection based on a convolutional neural network (CNN), red, green, blue and depth (RGB-D) information fusion and an accurate target location strategy. First, the UAV is equipped with an Intel Realsense D455 camera, which collects longan images from the front for training and testing the model. Second, the lightweight MobileNet backbone network is used to improve the performance of the You Only Look Once version 4 (YOLOv4) model in feature extraction. The results for the test set show that compared with the classical feature pyramid network (FPN), YOLOv3 and YOLOv4 models, this model reduces the computation, parameters and detection time of the model. Compared with MobileNet single-shot multibox detector (MobileNet-SSD) and YOLOv4-tiny, this model exhibits improved detection accuracy. Third, according to the target detection result map, a strategy is formulated to accurately determine the suitable picking point on the main branch of the result. Finally, the performance of the improved model and picking platform in the harvest scene is evaluated by performing picking experiments in a longan orchard. In summary, we fully exploit the advantages of the combination of UAVs, RGB-D cameras and CNNs to improve the speed and accuracy of target detection and location for longan picking by UAVs based on vision. [ABSTRACT FROM AUTHOR]