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Blueberries have high nutritional and economic value and are easy to cultivate, so they are common fruit crops in China. There is a high demand for blueberry in domestic and foreign markets, and various technologies have been used to extend the supply cycle of blueberry to about 7 months. However, blueberry grows in clusters, and a cluster of fruits generally contains fruits of different degrees of maturity, which leads to low efficiency in manually picking mature fruits, and at the same time wastes a lot of manpower and material resources. Therefore, in order to improve picking efficiency, it is necessary to adopt an automated harvesting mode. However, an accurate maturity detection model can provide a prerequisite for automated harvesting technology. Therefore, this paper proposes a blueberry ripeness detection model based on enhanced detail feature and content-aware reassembly. First of all, this paper designs an EDFM (Enhanced Detail Feature Module) that improves the ability of detail feature extraction so that the model focuses on important features such as blueberry color and texture, which improves the model's ability to extract blueberry features. Second, by adding the RFB (Receptive Field Block) module to the model, the lack of the model in terms of receptive field can be improved, and the calculation amount of the model can be reduced at the same time. Then, by using the Space-to-depth operation to redesign the MP (MaxPool) module, a new MP-S (MaxPool–Space to depth) module is obtained, which can effectively learn more feature information. Finally, an efficient upsampling method, the CARAFE (Content-Aware Reassembly of Features) module, is used, which can aggregate contextual information within a larger receptive field to improve the detection performance of the model. In order to verify the effectiveness of the method proposed in this paper, experiments were carried out on the self-made dataset "Blueberry—Five Datasets" which consists of data on five different maturity levels of blueberry with a total of 10,000 images. Experimental results show that the mAP (mean average precision) of the proposed network reaches 80.7%, which is 3.2% higher than that of the original network, and has better performance than other existing target detection network models. The proposed model can meet the needs of automatic blueberry picking. [ABSTRACT FROM AUTHOR]

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits. [ABSTRACT FROM AUTHOR]

Smart agricultural harvesting robots' vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the "eye–brain–hand" harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the "eye–brain–hand" picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production. [ABSTRACT FROM AUTHOR]

Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model's positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model's ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system's mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features. [ABSTRACT FROM AUTHOR]

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting. [ABSTRACT FROM AUTHOR]

Vibration harvesting is a promising method for walnut production owing to its low cost and high efficiency. However, current research focuses on simulation analysis and lacks a theoretical model explaining the walnuts' specific vibration response. This affects the design of the input vibration parameters during harvesting and thus reduces the harvesting efficiency. In this paper, a novel theoretical model of walnuts during vibration harvesting was established to analyze the vibration response, including the motion morphology (motion trajectory and dropping position) and detachment force. A field test was then carried out to verify the theoretical model. The theoretical and experimental results showed that the motion trajectory of the walnuts during vibration harvesting is similar to an ellipse, and the dropping position is at either of the two end points of the trajectory. The detachment force was found to be proportional to the vibration amplitude and the square of the vibration frequency theoretically. This paper provides a theoretical reference for designing the optimal input vibration parameters of a harvesting device to improve the harvesting rate of walnuts. [ABSTRACT FROM AUTHOR]

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model's attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems. [ABSTRACT FROM AUTHOR]

With respect to the optimal ripening time of the fruit, in recent decades there have been hundreds of works that study the optimum time for fruit harvesting based on its compositional and organoleptic characteristics [[1], [3]]. These original research papers can be grouped into two categories: (1) Evolution of compounds and characteristics of crops during ripening [[21], [23]]; (2) Evolution of compounds and characteristics of crops after harvest [[25], [27]]. Evolution of Compounds and Characteristics of Crops during Ripening In the first article, Szalóki et al. [[21]] determined the threshability of four local temperate japonica rice varieties during the ripening phase over three consecutive years with the application of an improved threshing meter. In this work, special emphasis has been placed on the details related to the changes that occur in vitamin C, phenolic compounds, flavonoids, glycosides, and volatile compounds, as well as the antioxidant activity during the drying process of fruits and plants. [Extracted from the article]

Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce's upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces. [ABSTRACT FROM AUTHOR]

Asparagus harvesting presents unique challenges, due to the variability in spear growth, which makes large-scale automated harvesting difficult. This paper describes the development of an asparagus harvesting robot system. The system consists of a delta robot mounted on a mobile track-based platform. It employs a real-time asparagus detection algorithm and a sensory system to determine optimal harvesting points. Low-level control and high-level control are separated in the robot control. The performance of the system was evaluated in a laboratory field mock-up and in the open field, using asparagus spears of various shapes. The results demonstrate that the system detected and harvested 88% of the ready-to-harvest spears, with an average harvesting cycle cost of 3.44 s ± 0.14 s . In addition, outdoor testing in an open field demonstrated a 77% success rate in identifying and harvesting asparagus spears. [ABSTRACT FROM AUTHOR]

Maize is one of the most important economic crops in the world. The integrity of maize grains directly affects the economic value of the maize as a main commodity that depends on the percentage of grains broken (POGB) in mechanized harvesting. Decreasing grain processing breakage is key to achieving mechanical harvesting with high quality. It is difficult to ensure a low POGB, because it depends on different maize varieties, their moisture contents, threshing devices and harvester working speeds. In this paper, the effects of these factors on the POGB are investigated when the working speed of the harvester is 1.0 m·s−1, 1.5 m·s−1 and 2.0 m·s−1, respectively. The different threshing forms, including tangential-axial-flow (TAF), axial-flow with nail-tooth (AFN), axial-flow with rasp bar in big space (AFRBBS) and axial-flow with rasp bar in small space (AFRBSS), are summarized. The POGB of TAF was 5.4% and it was the lowest of four threshing devices when the working speed of the harvester was 1.0 m·s−1, which was suitable to thresh maize at a low working speed. Maize Demeiya No. 1 (DMYN1), XianDa205 (XD205), Demeiya No. 3 (DMYN3) and Hayu189 (HY189) were harvested at different harvester working speeds, and the POGB of maize DMYN1 was the lowest among the four maize varieties. The POGB increased with increases in the working speed of the harvester and the moisture content of the maize grains. The POGB reached a minimum value of 3.5% when maize XD205 with the lowest moisture content was harvested. The results can provide a reference for choosing the maize variety, its moisture content, the threshing device and the working speed of the harvester for decreasing grain processing breakage. [ABSTRACT FROM AUTHOR]

During mechanized table grape harvesting, berries are subjected to vibration and collision, which can cause shedding and damage to the fruit. Research on table grape berry shedding has primarily focused on macroscopic swing modes, which are reflected in the integrated grape cluster structure and idealized particle interactions, as well as static response treatments. However, these approaches are unable to accurately explain the characteristics of berry wobbling during picking, predict shedding-prone areas, or identify factors affecting shedding. In this paper, we study the dynamic response characteristics of grape berries in the X, Y, and Z directions by establishing a dynamic model and combining harmonic response and random vibration characteristics with finite element analysis. Our studies revealed that grape berries exhibit various forms (swinging and rebounding) under the same stimulus during harvesting. The grape berry amplitude in the X, Y, and Z directions were 14.71, 12.46, and 27.10 mm, respectively, with the most obvious response being in the Z direction and the flattest response in the Y direction. Berries in the lower cob system part were relatively stable, while those in the upper right side were more prone to swinging and falling, with areas most likely to fall off concentrated in the upper right side. This system accurately predicted the dynamic response characteristics of fruit during vibration harvesting and provided an ideal basis for mechanized grape harvesting. Optimization and research on fruit collection equipment may benefit from this theoretical basis. [ABSTRACT FROM AUTHOR]

For high-speed robotic cut-and-catch harvesting, efficient trellis grape recognition and picking point positioning are crucial factors. In this study, a new method for the rapid positioning of picking points based on synchronous inference for multi-grapes was proposed. Firstly, a three-dimensional region of interest for a finite number of grapes was constructed according to the "eye to hand" configuration. Then, a feature-enhanced recognition deep learning model called YOLO v4-SE combined with multi-channel inputs of RGB and depth images was put forward to identify occluded or overlapping grapes and synchronously infer picking points upwards of the prediction boxes of the multi-grapes imaged completely in the three-dimensional region of interest (ROI). Finally, the accuracy of each dimension of the picking points was corrected, and the global continuous picking sequence was planned in the three-dimensional ROI. The recognition experiment in the field showed that YOLO v4-SE has good detection performance in various samples with different interference. The positioning experiment, using a different number of grape bunches from the field, demonstrated that the average recognition success rate is 97% and the average positioning success rate is 93.5%; the average recognition time is 0.0864 s; and the average positioning time is 0.0842 s. The average positioning errors of the x, y, and z directions are 2.598, 2.012, and 1.378 mm, respectively. The average positioning error of the Euclidean distance between the true picking point and the predicted picking point is 7.69 mm. In field synchronous harvesting experiments with different fruiting densities, the average recognition success rate is 97%; the average positioning success rate is 93.606%; and the average picking success rate is 92.78%. The average picking speed is 6.18 s × bunch − 1 , which meets the harvesting requirements for high-speed cut-and-catch harvesting robots. This method is promising for overcoming time-consuming harvesting caused by the problematic positioning of the grape stem. [ABSTRACT FROM AUTHOR]

Algae are currently used in many areas, including dietetics, pharmacy, cosmetology and to increase the nutritional value of food and animal feed due to their chemical composition. They are a source of extremely valuable molecules, including polyunsaturated fatty acids and pigments. Algae are also a valuable source of protein and almost all essential vitamins. They are rich in pigments such as chlorophyll, carotenoids and phycobiliproteins. These substances have a wide range of commercial applications. Due to its very intensive use, the demand for this plant raw material is constantly growing. Therefore, the methods of growing and harvesting algae are constantly improved in order to maximize the cultivation effect while minimizing costs and energy inputs. Future research should focus on improving algae cultivation and harvesting systems, with an emphasis on the possibility of genetic modifications that would allow even more efficient algae cultivation. This review summarizes methods of micro- and macroalgae cultivation, the chemical composition of selected algae species, which are important from the pharmaceutical, dietary and cosmetic points of view and therapeutic and dietary applications of compounds derived from different algae species. The key conclusion drawn from this article is that algae are an extremely valuable raw material, rich in numerous macro- and micronutrients necessary for humans, the acquisition of which is part of the currently important strategy of ecological policy for obtaining raw materials for various industries. [ABSTRACT FROM AUTHOR]

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic. [ABSTRACT FROM AUTHOR]

Standardization of crop yield estimation methods at various levels of farming helps to obtain accurate agricultural statistics as well as assessing the suitability of agricultural practices under various production conditions. The current paper reviews various maize yield estimation methods, taking into account available yield parameters, and it also analyses the yield gap between maize potential and attainable yield. The easiest and more reliable methods of yield estimation are based on yield parameters collected from the field. However, farmer estimation methods are cheaper and faster compared to any other method of yield estimation from farmers' fields. This paper also elaborates on the importance of the use of more complex methods for yield estimation, such as remote sensing and crop modelling. These complex methods are more accurate and can predict yield before field harvest with less deviation from the exact harvest yield. However, they are very expensive and not efficient for small plots of land (less than 1 ha). Factors that contribute to the gap between potential and actual yield include poor implementation of agricultural policies, strict regulation of fertilizer inputs, vulnerability of smallholder cropping systems to adverse climatic conditions, occurrence of biotic and abiotic constraints, as well as unavailability of seeds and labor. [ABSTRACT FROM AUTHOR]

Biomass and nitrogen (N) accumulation in intercrops for forage production under different fertilizations have seldom been assessed and, more occasionally, have been investigated at different stages. The biomass and N contents of barley (Hordeum vulgare L.) and field bean (Vicia faba L. var minor) grown as sole crops and intercrops, with five N rates from 0 to 200 kg ha−1, both at the heading and early dough stage of the cereal, for green forage and silage purposes, were determined in a two-year field experiment in Central Italy. We discovered differences between the two harvests both in their biomass production and N content as, at the latter stage, they, respectively, increased by 27 and 13%. The sole and intercropped crops showed differences at the two stages, essentially in the response of their inflorescences. N fertilization was more effective at the latter stage, because of the barley's response to the nutrient availability. However, while nitrogen use efficiency improved with crop ageing, the values of the land equivalent ratio and the nitrogen land equivalent ratio declined; thus, a better complementarity in the use of land and N between the two species seemed to be achieved when the intercrop was grown for green forage purposes. [ABSTRACT FROM AUTHOR]

In pome fruit cultivation, apples and pears need to be handled in various processes such as harvesting and sorting. Currently, most processes require a vast amount of manual labor. Combined with a structural shortage of seasonal workers, innovation in this field is crucial. Automated processes could provide a solution wherein the search for an appropriate manipulation tool is essential. Aside from several grippers, customized for harvesting by various researchers, the industry also provides a wide variety of standardized manipulation tools. This paper benchmarks a wide set of the most relevant gripping principles, primarily based on their ability to successfully handle fruit, without causing damage. In addition, energy consumption and general feasibility are evaluated as well. The performed study showed that the customized foam gripper scores the overall best for all test scenarios at the cost of being the least energy efficient. Furthermore, most other gripping tools excelled at certain specific tasks rather than being generally deployable. Impactive grippers are better suited for harvesting at low energy consumption, while astrictive grippers are more suited for sorting tasks constricted by the available space. The results also showed that commercially available soft grippers are not always capable of handling sensitive fruits such as pears without causing damage. [ABSTRACT FROM AUTHOR]

Today, the post-harvest root stubble treatment of kale in Hunan mostly uses manual pulling and centralized treatments, which are inefficient and labor-intensive. In this study, to realize the direct mechanical crushing of kale root stubble and return it to the field after harvesting, we established an accurate simulation model of kale root stubble by creating a model of the root stubble of kale and calibrating the parameters of the simulation. This study took Jingfeng No. 1 kale stubble as the research object and used EDEM2021.2 simulation software to study the parameters of the kale stubble-crushing simulation model. The peak shear force of the sheared kale root stubble was used as the test data, and the most significant factors affecting the shear force were screened out through the Plackett–Burman test for the Design-Expert design. In addition, the steepest climb test and Box–Behnken test were used to accurately assess the factor data to obtain the best simulation value, which was 861.02 N. The relative error between the simulated and measured values was 0.61%. Finally, an accurate simulation stubble model was established by combining the best simulation parameters with the measured stubble length and diameter. This model provides a theoretical basis and technical support for more in-depth research on stubble simulation and mechanized stubble return. [ABSTRACT FROM AUTHOR]

Cotton mechanized harvesting is the development direction of cotton production. The rapid development of mechanized cotton harvesting in Xinjiang has significantly increased the efficiency of cotton harvesting and reduced the harvesting cost. However, in the rapid development of mechanized cotton harvesting, there are also the problems of net yield, recovery rate and poor harvesting quality, which lead to the poor quality competitiveness of mechanized cotton harvesting. In order to solve the problem of mechanized cotton loss, the key is to reduce the problem of cotton miscellaneous, and breed cotton varieties suitable for mechanized harvesting. The purpose of this study was to clarify the key trait correlation of defoliation through the establishment and association analysis of foliation and deciduous phenotype indicators in cotton. In this study, the phenotypic indexes of defoliation and deciduous traits were established through the comprehensive analysis of the defoliation rate of 273 cotton germplasm resources and other 11 related phenotypic traits in the field, in order to provide guidance for the breeding and production of cotton varieties collected by breeders. In addition to peeling rate, an analysis of the association between 11 agronomic parameters and peeling rate and hanging rate revealed that the number of effective branches, chlorophyll SPAD value, fruit branch angle, and hanging rate have substantial correlation in 3 years. Hence The hanging rate, fruit branch angle, effective branch number, and chlorophyll SPAD value can be used as the evaluation indicators of varieties for peeling ability trait index. [ABSTRACT FROM AUTHOR]

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant's continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah. [ABSTRACT FROM AUTHOR]

The Peiligang culture (ca. 9000–7000 cal. BP) represents the first Neolithic settlements in the middle Yellow River Valley, marking the beginning of millet and rice farming in the region. While previous studies have focused primarily on identifying cultivated cereals, less attention has been given to plant harvesting and processing practices or environmental conditions. To address this gap, we present new phytolith data from the Peiligang site (8000–7600 cal. BP) to make three key contributions. First, we show that the Peiligang people utilized two microhabitats: hillslopes for dryland millet cultivation and alluvial plain for wetland resources. Second, we combine our findings with other archaeological evidence to reconstruct the environmental conditions of the Peiligang site, suggesting that it was a water-rich habitat. Finally, by analyzing phytolith remains of plant processing waste in middens, we reconstruct how people harvested and processed millets and rice at the site. This study sheds light on the plant-based subsistence strategies employed by the Peiligang people and offers insights into the environmental factors that contributed to the development of early farming in the middle Yellow River Valley. [ABSTRACT FROM AUTHOR]

In order to accurately establish a discrete element model for the whole plant flexibility of upright rice during the harvesting period, several physical characteristics, such as geometric features, moisture content, and density, of the entire rice plant were measured, along with frictional properties, such as the static and rolling friction coefficients, and mechanical properties, including the elastic modulus and restitution coefficient. A flexible and upright discrete element model of the rice plant was established using the DEM method based on the Hertz–Mindlin (no slip) and Hertz–Mindlin with bonding mechanical models. The parameters were optimized through Plackett–Burman screening experiments, steepest ascent experiments, and Box–Behnken optimization experiments to accurately determine the discrete element model parameters of each component of the rice plant. The calibration process of the contact parameters between rice grains and steel was analyzed in detail as an example, resulting in a calibration error of 0.68% for the natural repose angle. Taking the calibration of the contact parameters between the main stem and steel as an example, a detailed analysis of the calibration process was conducted. The calibration resulted in a calibration error of 2.76% for the natural repose angle and 2.33% for deflection. This study lays the foundation for understanding the mechanical response of rice and machinery when they are coupled together. Additionally, it provides valuable references for establishing discrete element models of plant species other than rice. [ABSTRACT FROM AUTHOR]

Optimizing nitrogen (N) inputs is crucial for maximizing wheat yield and ensuring environmental sustainability. Wheat's economic significance in India calls for a comprehensive evaluation of its ecological implications to develop a resilient production system. This study aimed to identify and evaluate ten wheat cultivars for their yield and N-use efficiency under varying nitrogen inputs (control (N0), half of the recommended nitrogen (N75), and the recommended nitrogen (N150)) using the surface application of neem-oil-coated urea. All N inputs were applied in three splits, basal, crown root initiation, and tillering stages, and an experiment was conducted in a split-plot design. The application of N150 gave the highest dry matter accumulation (DMA) at harvesting stage (AHS) (871 g m−2), seed/spike (60), grain yield (GY = 7.4 t ha−1), straw yield (SY = 8.9 t ha−1), harvest index (HI = 45.2%), protein (12.5%), and total uptake of N (TUN) (223 kg ha−1) by the cultivar 'HD 3249', being closely followed by the cultivar 'HD3117'. Six cultivars ('HD 3298', 'HD 3117', 'HD 3249', 'PBW 550', 'HD 3086', 'HD 2967') out of the ten cultivars evaluated responded well to different input treatments with respect to the grain yield efficiency index (GYEI ≥ 1). Regarding N input, N75 and N150 recorded the highest increases in plant height, AHS (16.5%; 21.2%), dry matter accumulation (DMA) at 30 days after sowing (DAS) (37.5%; 64%), DMA-60 DAS (42%; 53%), DMA-90 DAS (39.5%; 52.5%), TILL-30 DAS (19.8%; 26.4%), TILL-60 DAS (33.3%; 44%), TILL-90 DAS (37.2%; 47.2%), seed/spike (8%; 10%), 1000-grain weight (7.8%; 12.2%), and protein content (23.3%; and 33%) when compared with N0. Furthermore, the application of N75 and N150 improved GY (72.1%; 142.6%), SY (61.1%; 110.6%), BY (65.5%; 123%), and HI by 4.4% and 9%, respectively, over N0. Nitrogen addition (N75 and N150) also significantly increased total nitrogen uptake (104.7%; 205.6%), respectively, compared to N0. The correlation analysis revealed a positive association among most of the crop parameters. Overall, our research results suggest that the cultivars 'HD 3249' and 'HD 3117' have the potential to be effective options for improving N utilization efficiency, grain yield, and GYEI in North-West India. [ABSTRACT FROM AUTHOR]

This study aims to improve the Agaricus bisporus detection efficiency and performance of harvesting robots in the complex environment of the mushroom growing house. Based on deep learning networks, an improved YOLOv5s algorithm was proposed for accurate A. bisporus detection. First, A. bisporus images collected in situ from the mushroom growing house were preprocessed and augmented to construct a dataset containing 810 images, which were divided into the training and test sets in the ratio of 8:2. Then, by introducing the Convolutional Block Attention Module (CBAM) into the backbone network of YOLOv5s and adopting the Mosaic image augmentation technique in training, the detection accuracy and robustness of the algorithm were improved. The experimental results showed that the improved algorithm had a recognition accuracy of 98%, a single-image processing time of 18 ms, an A. bisporus center point locating error of 0.40%, and a diameter measuring error of 1.08%. Compared with YOLOv5s and YOLOv7, the YOLOv5s-CBAM has better performance in recognition accuracy, center positioning, and diameter measurement. Therefore, the proposed algorithm is capable of accurate A. bisporus detection in the complex environment of the mushroom growing house. [ABSTRACT FROM AUTHOR]

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively. [ABSTRACT FROM AUTHOR]

Changing the harvesting time of apples from the optimum harvest window to delayed harvesting may be applied if it is impossible to harvest apples at the optimal time, but it requires changing other factors, as they influence the quality of fruits and shelf life. The aim of the study was to assess the quality of 'Red Jonaprince' apples during storage after delayed harvesting and 1-methylcyclopropene (1-MCP) preharvest and postharvest treatment for various storage times. Apples were studied within four groups subjected to preharvest and postharvest treatments, as follows: Group 0—no 1-MCP treatment; Group 1—1-MCP preharvest treatment; Group 2—1-MCP postharvest treatment; and Group 3—1-MCP preharvest and postharvest treatment. All apples were subjected to ultra-low oxygen (ULO) storage conducted for 3, 5 or 6 months, while the analyses were conducted directly after ULO storage (simulated shelf life—0 days) and after simulated shelf life (7 days). For firmness, in the case of 1-MCP applied only preharvest (Group 1) and only postharvest (Group 2), before shelf life, the longer ULO storage resulted in obtaining lower values of firmness (p < 0.0001). If 1-MCP was not applied postharvest (Group 0 and Group 1), and short ULO storage was applied (3 and 5 months for Group 0; 3 months for Group 1), after shelf-life lower values of firmness were observed (p < 0.0001). For soluble solids content (SSC), in the case of 1-MCP not applied preharvest (Group 0 and Group 2), before shelf life, and for 1-MCP applied postharvest (Group 2) after shelf life, the longer ULO storage resulted in obtaining lower values of SCC (p < 0.0001). For titratable acidity (TA), in the case of all the studied groups after shelf life, as well as in case of 1-MCP applied only preharvest (Group 1) also before shelf life, the longer ULO storage resulted in obtaining lower values of TA (p < 0.0001). Except for the 1-MCP applied only postharvest (Group 2), in the case of short ULO storage applied (3 and 5 months for Group 0; 5 months for Group 1; 5 months for Group 3), after shelf-life lower values of TA were observed (p < 0.0001). If delayed harvesting must be conducted, applying 1-MCP not only postharvest, but also preharvest, allows obtaining the most stable firmness and SSC, which do not decrease during storage and shelf life. Taking this into account, it may be concluded, that in the case of delayed harvesting, combining 1-MCP applied preharvest and postharvest should be recommended to keep the quality parameters stable during storage and shelf life. [ABSTRACT FROM AUTHOR]

This paper focuses on the adverse effects of soil sodicity and alkalinity on the growth of barley (Hordeum vulgare L.) in a rainfed environment in south-western Australia. These conditions cause the accumulation of salt (called 'transient salinity') in the root zone, which decreases the solute potential of the soil solution, particularly at the end of the growing season as the soil dries. We hypothesized that two approaches could help overcome this stress: (a) improved micro-water harvesting at the soil surface, which would help maintain soil hydration, decreasing the salinity of the soil solution, and (b) soil amelioration using small amounts of gypsum, elemental sulfur or gypsum plus elemental sulfur, which would ensure greater salt leaching. In our experiments, improved micro-water harvesting was achieved using a tillage technique consisting of exaggerated mounds between furrows and the covering of these mounds with plastic sheeting. The combination of the mounds and the application of a low rate of gypsum in the furrow (50 kg ha−1) increased yields of barley grain by 70% in 2019 and by 57% in 2020, relative to a control treatment with conventional tillage, no plastic sheeting and no amendment. These increases in yield were related to changes in ion concentrations in the soil and to changes in apparent electrical conductivity measured with the EM38. [ABSTRACT FROM AUTHOR]

The cultivation of lemon balm (Melissa officinalis L.) is necessary for pharmaceutical and cosmetic production. The aim of our work was to investigate the effect of the plant density as well as the harvesting method on the yield and quality characteristics of M. officinalis, which is used as a source of polyphenols and essential oil in the pharmaceutical, food, and cosmetic industries. The field experiment was carried out in two growing seasons (2019 and 2020). The experiment was set up as a 2-factor experiment; the factors studied were the plant spacing (30 × 30 cm or 40 × 40 cm) and the harvesting method (single harvesting or double harvesting). The lemon balm herb was cut twice (in mid-July and early September) or once (in early September) depending on the combination. The air-dried leaves were subjected to laboratory tests to determine the essential oil, total tannin, and total flavonoid contents. The yield of lemon balm in the individual years in the study depended more on the harvesting method than on the plant density. The essential oil content was 0.25–0.38% depending on the plant density. Considering the yields of the fresh and air-dried herb and leaves, essential oil yield and oil, and tannin and flavonoid contents, a higher plant density (40 × 40 cm) is a better agronomic option than a lower density (30 × 30 cm). Harvesting the herb twice proved to be more efficient in terms of quantity and quality than a single harvest. [ABSTRACT FROM AUTHOR]

Given the problem of climate change and the requirements laid down by the European Union in the field of gradual decarbonization of production, it is necessary to implement solutions of reducing greenhouse gas (GHG) emissions into agricultural practice. This research paper aimed to evaluate the carbon footprint and life-cycle costs of grain maize production in various tillage systems. The material for the analyses was data from 2015–2017 collected on 15 farms located in the Wielkopolska region (Poland) and growing maize for grain in three tillage systems: conventional, reduced, and no-tillage. The life-cycle assessment and life-cycle costing methodologies were applied to assess the GHG emissions and costs associated with the grain maize production in the stages from "cradle-to-farm gate", i.e., from obtaining raw materials and producing means for agricultural production, through the processes of maize cultivation to grain harvesting. The calculated values of the carbon footprint indicator for maize production in conventional, reduced, and no-tillage systems were 2347.4, 2353.4, and 1868.7 CO2 eq. ha−1, respectively. The largest source of GHG emissions was the use of nitrogen fertilizers. Non-inversion tillage with cover crops and leaving a large amount of crop residues in the field increased the sequestration of organic carbon and contributed to a significant reduction of the carbon footprint in maize production. The conventional tillage system demonstrated the highest overall life-cycle costs per hectare. [ABSTRACT FROM AUTHOR]

Protected cultivation systems such as greenhouses are becoming increasingly popular globally and have been adopted because of unpredictable climatic conditions and their ability to easily control micro- and macroenvironments. However, limitations such as hazardous work environments and shortages in labor are major concerns for agricultural production using these structures. This has led to the development and adoption of robotic systems. For the efficient use of robots in protected cultivation systems, we formulate the work efficiency problem and model a three-dimensional standard strawberry greenhouse to analyze the effectiveness of a strawberry-harvesting robot compared to different levels of human workforce (experienced, average, and beginner). Simulations are conducted using Quest software to compare the efficiency of different scenarios of robotics to humans. Different methods of improvement from battery capacity and charge rate to harvesting speed are investigated and optimal conditions are recommended. The average hourly production of the robot is about five times lower than that of skilled workers. However, robots are more productive due to their ability to work around the clock. Comparative analyses show that a reduction in harvesting time per strawberry from 3 to 1 s would result in an increase in daily production from 347.93 to 1021.30 kg. This would lead to a five-fold increase in comparison to present daily production. A 10% improvement in battery charge time would result in the battery capacity gaining two extra hours from the current 10 h and would cut the current 2 h needed for charge to 1 h. This paper proposes an operation process and suggestions for changes needed for improving the work efficiency of robots in a greenhouse. This could be extended to other crops and greenhouses. [ABSTRACT FROM AUTHOR]

By 2022, the total area of super high-density (SHD) almond orchards worldwide had spread to 14,622 ha. As well as factors such as spacing, and tree architecture, proper row orientation should ensure a more efficient sun exposure, yield and harvesting. This research was conducted in an irrigated SHD almond orchard of cv. Lauranne® Avijor in Andria, Italy, in 2021 and 2022 (at 4th and 5th year from planting, respectively) to gain a deeper understanding on the effects of two row orientations on available Photosynthetic Active Radiation (PAR), Leaf Area Index (LAI), canopy growth, yield and harvesting efficiencies in this new planting system. The east-west row orientation in a SHD almond orchard negatively impacted LAI, canopy growth, yield, fruit quality and yield efficiencies, especially in the second year. The north-south row orientation allowed a better PAR distribution inside the whole canopy than east-west row orientation. This unevenness in PAR distribution inside the whole east-west oriented canopies could represent the driving engine of the effect on growth and yield parameters. Although some quality parameters differed between exposures, they all remained within the standard range of the cultivar. The results provide a scientific basis for improved SHD orchard management, helping to keep them as Sustainable and Efficient Systems (SES). [ABSTRACT FROM AUTHOR]

In harvesting operations, simulation verification of hand–eye coordination in a virtual canopy is critical for harvesting robot research. More realistic scenarios, vision-based driving motion, and cross-platform interaction information are needed to achieve such simulations, which are very challenging. Current simulations are more focused on path planning operations for consistency scenarios, which are far from satisfying the requirements. To this end, a new approach of visual servo multi-interaction simulation in real scenarios is proposed. In this study, a dual-arm grape harvesting robot in the laboratory is used as an example. To overcome these challenges, a multi-software federation is first proposed to establish their communication and cross-software sending of image information, coordinate information, and control commands. Then, the fruit recognition and positioning algorithm, forward and inverse kinematic model and simulation model are embedded in OpenCV and MATLAB, respectively, to drive the simulation run of the robot in V-REP, thus realizing the multi-interaction simulation of hand–eye coordination in virtual trellis vineyard. Finally, the simulation is verified, and the results show that the average running time of a string-picking simulation system is 6.5 s, and the success rate of accurate picking point grasping reached 83.3%. A complex closed loop of "scene-image recognition-grasping" is formed by data processing and transmission of various information. It can effectively realize the continuous hand–eye coordination multi-interaction simulation of the harvesting robot under the virtual environment. [ABSTRACT FROM AUTHOR]

The nutrient and nutraceutical quality of greenhouse wild rocket is strongly influenced by the light environment and nitrogen fertilization. We investigated the effects of two cover materials, a diffuse light film (Film1) and a traditional clear film (Film2), and three nitrogen regimes, no N supply (N0) and sub-optimal (N1) and optimal (N2) doses, also in combination with a biostimulant (Stimolo Mo), on the mineral composition, antioxidant properties and chlorophyll and carotenoid content of rocket plants grown in the autumn–spring cycle. The leaf concentration of most of the minerals was higher under Film1 compared to Film2. In general, K, Ca, Mg and Na were higher, and S was lower in the presence of N supply, and the addition of the biostimulant promoted the mineral uptake. Under Film1, the hydrophilic antioxidant activity (HAA) was higher in some harvests, and the ABTS antioxidant activity (ABTS AA) in the first one, while always lower afterward, than under Film2. Nitrogen fertilization did not affect the antioxidant activity, while it reduced the content of total phenols and ascorbic acid. The biostimulant application increased ABTS AA at the optimal N dose and reduced total phenols in unfertilized plants. Both the diffuse light and the N supply inhibited the synthesis of ascorbic acid, while N fertilization and the biostimulant promoted the synthesis of chlorophylls. The experimental treatments exerted variable effects over time and significant interactions with the harvest period were found for many of the investigated parameters. [ABSTRACT FROM AUTHOR]

The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning, this article summarizes and analyzes some representative methods. This article also demonstrates the current challenges and future potential developments. This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning. [ABSTRACT FROM AUTHOR]

Whole-plant corn silage (WPCS) is a major source of forage for dairy cattle in Brazil. Improved kernel processing may be especially advantageous when feeding corn hybrids with vitreous endosperms. Two experiments were conducted to evaluate the effects of the theoretical length of cut (TLOC) and ensiling time on WPCS particle size and kernel processing with two types of forage harvesters. In the first trial, the plant was harvested by a pull-type forage harvester (PTFH) at TLOCs of 3, 6 and 9 mm. In the second trial, the harvesting was performed by a self-propelled forage harvester (SPFH) at TLOCs of 6, 12 and 18 mm, with a roll gap of 3 mm. The WPCS were stored for 0, 35 and 140 days. In the PTFH trial, the TLOCs of 3 and 6 mm did not affect the WPCS particle size distribution and mean particle length (MPL). However, the TLOC of 9 mm increased the MPL. The increase in the TLOC with the SPFH led to a higher MPL and percentage of long particles. The ensiling time increased the MPL and long particles only for the WPCS harvested by the SPFH. The strategy of reducing the TLOC with the SPFH increased the percentage of kernels smaller than 4.75 mm. Furthermore, the TLOC of 6 mm led to the best kernel processing with the SPFH. The ensiling time reduced the particle size of the kernel fractions for both forage harvesters. The corn silage processing score was improved with 140 days of ensiling with the SPFH. These findings suggest that increasing the ensiling time and a low TLOC in SPFHs (6 mm) may be good strategies to increase kernel damage and starch digestibility in WPCS. [ABSTRACT FROM AUTHOR]

In the dynamic and unstructured environment where horticultural crops grow, obstacles and interference frequently occur but are rarely addressed, which poses significant challenges for robotic harvesting. This work proposed a tactile-enabled robotic grasping method that combines deep learning, tactile sensing, and soft robots. By integrating fin-ray fingers with embedded tactile sensing arrays and customized perception algorithms, the robot gains the ability to sense and handle branch interference during the harvesting process and thus reduce potential mechanical fruit damage. Through experimental validations, an overall 83.3–87.0% grasping status detection success rate, and a promising interference handling method have been demonstrated. The proposed grasping method can also be extended to broader robotic grasping applications wherever undesirable foreign object intrusion needs to be addressed. [ABSTRACT FROM AUTHOR]

Farmers may promote the cultivation of under-sown cover crops (CCs) in various ways without jeopardizing the yield of a cash crop. With this survey, we aimed to understand how Finnish farmers manage possible challenges with under-sown CCs. A farmer survey was carried out in 2021. We invited 6493 farmers who had selected CCs as a registered measure to answer a questionnaire with 20 statements (a Likert scale), and 1130 responded (17.4%). A Cochran–Mantel–Haenszel test was used to measure the strength of the association between 11 farm/farmer characteristics of the respondents and 20 statements. Responses indicated that farmers often took under-sown CCs into account during the growing season. Sowing was considered an especially critical measure and the CC seeding rate was often assessed with a test run before sowing. Thirty-nine per cent of the respondents had made investments, most often to facilitate sowing. The farmers usually adjusted the fertilizer rate only according to the cash crop. Early harvesting of a cash crop was considered important by 58% of farmers to ensure that the CCs do not hamper the harvest. Farmers harvested cash crops as soon as they matured and were harvestable, though they had mixed views on whether CCs impacted the quality of the cash crop. Subsidized, investing farmers were likely to be oriented towards the benefits from the ecosystem services provided by CCs. Their experiences should be shared among the farming community to support the large-scale implementation of CCs. [ABSTRACT FROM AUTHOR]

Manually harvesting hydroponic leafy vegetables from a cultivation pipeline is labor-intensive and expensive. Rapidly grabbing hydroponic leafy vegetables grown in different positions and orientations in the planting hole is the primary issue for efficient mechanical harvesting. Thus, a novel grabbing mechanism with double-pivot rotation cross fingers is proposed. The fingers' inner surfaces could envelop the grabbable area of the leafy vegetable in the grasping process and position each leafy vegetable stalk to the center of the planting hole before taking it out. A grabbing mechanism for harvesting hydroponic Chinese kale was designed and optimized with less than 1 mm of grasping error, enough enveloping range, and no collision with the extended leaves. Laboratory experiments were conducted to investigate centering and grabbing at different initial positions and inclination angles of the hydroponic Chinese kale and varied finger deflection speeds. It was indicated that for the grasping inclination angle and grabbing success rate, the initial inclination angle was a significant factor, as was the position, whereas the finger deflection speed was insignificant. As the initial inclination angle of matured hydroponic Chinese kale in different initial positions is mostly larger than 60°, the best results were achieved with a finger deflection speed in the range of 40° s−1 to 60° s−1 and grasping inclination angles of 85° to 95°, with a grabbing success rate of more than 95%. This showed the promising applicability of the studied grabbing mechanism for harvesting hydroponic Chinese kale or other varieties of leafy hydroponic vegetables with similar growth characteristics. [ABSTRACT FROM AUTHOR]

Potato stem removal is one of the critical technical problems of potato mechanized harvesting; it directly affects the quality of potato harvesting and potato storage. There have been several studies on potato stem removal mechanisms. In practice, however, it was found that the potato stem removal rate was greatly influenced by the posture of the stem before it entered the removal mechanism. In this study, we designed a potato stem posture adjustment mechanism consisting of elastic curtains. A test rig was built to investigate the effect of curtain height, curtain width, and curtain suspension height on potato passage rate and potato stem removal rate. The Box–Behnken design (BBD), combined with the response surface method, was used to conduct the test. The optimal construction and installation parameters for each elastic curtain were determined as 278.93 mm for the curtain height, 20 mm for the curtain width, and 260 mm for the curtain suspension height. The predicted values of potato passage rate and potato stem removal rate under the optimal parameters were 92.36% and 82.83%, which were consistent with the validation test results. Based on the optimization results, a rigid-flexible coupled simulation model for a potato stem transported-posture adjustment process based on Abaqus and Adams was constructed. The maximum impact of the elastic curtain of the stem posture adjustment mechanism on the potato stem was 15.91 N and caused the stem to spring back. The projection angle β′ of the stem posture angle in the xoz plane before posture adjustment was 19.07°, and the β′ of the stem after posture adjustment was 87.18°. At this time, the stem was basically parallel to the rod of the separating sieve and had a high probability of falling from the gap of the bar to complete the removal of the stem. Overall, the stem transport position adjustment mechanism effectively adjusted the stem transported posture and improved the stem removal rate in potato mechanical harvesting. [ABSTRACT FROM AUTHOR]

The increase in the demand for almonds, the development of novel self-fertile and late-flowering varieties, and the establishment of plantations in new irrigated areas have led to significant progress in the productive techniques of almond tree cultivation. One of the most important has been the increase in planting density, due to the development of dwarfing rootstocks. This paper presents a comparison between two training systems with 'Soleta' almond cultivar: a super high density (SHD) system using Rootpac-20 dwarfing rootstock versus an open-center training system using GF-677 rootstock. To this end, several parameters related to chlorophyll content (fluorescence and SPAD) and light interception (from photosynthetically active radiation (PAR) measurements) were monitored throughout two vegetative cycles, and other productive conditions (flowering, fruit set and production) were tracked at specific times of the cycle. The open-center system resulted in higher PAR interception than the SHD system, but also in the presence of poorly illuminated fractions of the canopy. Differences were observed between both systems in terms of average fruit weight and yield per canopy volume. Lower yields were obtained in SHD system than in open-center, which may be significantly increased by adapting the inter-row spacing. However, the degree of efficiency in the use of resources or productive inputs, such as irrigation, was favorable to the new SHD training system, so its potential to become a reference system in modern plantations (using over-the-row harvesters similar to those used for vine and olive trees) seems promising. [ABSTRACT FROM AUTHOR]

With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture the perishable property of vegetables, and model it as a quadratic postharvest quality deterioration function. Then, we incorporate the postharvest quality deterioration function into the integrated harvest and farm-to-door distribution scheduling and formulate it as a quadratic vehicle routing programming model with time windows. Next, we propose a genetic algorithm with adaptive operators (GAAO) to solve the model. Finally, we carry out numerical experiments to verify the performance of the proposed model and algorithm, and report the results of numerical experiments and sensitivity analyses. [ABSTRACT FROM AUTHOR]

This work assesses the costs of exploiting the biomass feedstock chaff. Chaff is a harvest residue generated during the conventional grain harvesting process and usually remains on the field. In this paper, the costs of collecting and supplying chaff to the end user with different harvesting methods and supply chains are analyzed. The costs are estimated for a base case defining a set of general assumptions. The impact of these assumptions is analyzed in a sensitivity analysis by means of tornado diagrams. A full costing method based on the VDI guideline 2067 part 1 is applied for the cost estimation. The cost analysis reveals that ceasing the fractioning of grain, straw and chaff during harvesting and transporting them as a mixture reduces the harvesting costs significantly. The costs are decreased due to a reduction in agricultural operations and processing large amounts of material. The lowest total costs originate from the production of chaff-straw bales. Harvesting chaff as a single fraction leads to the highest costs with the investigated supply chains. Comparing the costs of chaff supply to potential revenues shows that an exploitation of the harvest residue can be economically feasible. [ABSTRACT FROM AUTHOR]

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting. [ABSTRACT FROM AUTHOR]

In order to improve the operational performance of the self-developed 4HLB-4 half-feed four-row peanut combine harvester, single-factor and multifactor field tests were conducted on key operational parameters affecting the quality of harvesting. The response surface methodology was used to study the effects of the forward speed, clamping height, peanut picking roller rotational speed, and cleaning sieve vibrational frequency on the loss rate and impurity rate. A response surface regression model of the loss rate and impurity rate was constructed. A multi-objective integrated optimization was carried out for each factor. The results showed that, in terms of the order of significance, the influence of each factor on the impurity rate was as follows: cleaning sieve vibrational frequency > clamping height > forward speed > peanut picking roller rotational speed. The order of significance of the influence of each factor on the loss rate was as follows: peanut picking roller rotational speed > forward speed > clamping height > cleaning sieve vibrational frequency. The optimal combination of parameters was a forward speed of 0.85 m/s, clamping height of 190 mm, peanut picking roller rotational speed of 550 rpm, and cleaning sieve vibrational frequency of 590 cpm. Under these conditions, the impurity rate of 2.62% and the loss rate of 2.05% were obtained, which effectively reduced the impurity rate and loss rate and met the quality requirements of Chinese peanut mechanized harvesting operations. The results of this study can provide a basis for the improvement and optimization of the 4HLB-4 half-feed four-row peanut combine harvester and the optimization of its operating parameters. [ABSTRACT FROM AUTHOR]

Current postharvest activities in the sesame value chain are prompting colossal losses, which reduce overall global productivity. This review portrays losses in sesame during various processing stages, from grain harvesting to marketing and transformation of crop seed into oil. Such losses in sesame not only reduce yield but also have an impact on the economy of its production territories. The loss in productivity is because the majority of farmers don't use adequate harvesting, packaging, or handling technologies to manage on-farm produce. Also, there is a lack of knack for minimizing postharvest losses. Therefore, the study penlights the inevitability of increasing production by raising productivity and quality while giving mitigation strategies to reduce postharvest losses. Elevating standardized productivity with accurate postharvest management is the only substitute for the gap between the global productivity average and the overall production potential of sesame. [ABSTRACT FROM AUTHOR]

The size of the microbial biomass and the activity of soil enzymes are among the most sensitive indicators of agricultural land quality. The aim of this study was to determine the spatial and temporal variability of microbial biomass, the activity of dehydrogenase (DHA) enzyme and the concentration of micro- (Na, Mg and Ca) and macroelements (Cu, Zn, Mn and Fe) in the soil, collected from 37 measurement sites (depth 0–30 cm) in a 40-hectare field during two growing seasons (wheat and oilseed rape). The percentage of nitrogen (%N) in the wheat grain and rapeseeds was also determined. Mapping the spatial distribution of the microbial biomass, the level of DHA activity and the concentration of the selected elements was used to assess the soil productivity. All tested soil parameters exhibited temporal and spatial variability. The creation of raster maps showing the distribution of the tested parameters allowed the observation of a higher nitrogen content in wheat grains in the south-western part of the field, with high values of DHA activity, bacterial biomass and soil pH. In the case of oilseed rape, plants grown in the northern part of the field were characterized by a higher nitrogen content in the grain, where the soil was characterized by a higher content of fungal biomass. On the basis of the obtained research results, a positive, statistically significant correlation was also shown between the biomass of bacteria and the level of DHA in the soil under the cultivation of both wheat and rape. The cultivation of both crops had a significant impact on the size of the microbial biomass pool and on the DHA activity level but did not affect the concentration of the nutrients in the soil. High concentrations of the analyzed elements at the measuring points correlated with a greater %N content in the grain/seeds of the crops harvested at those locations in the field. The results conclude that the mapping of the physicochemical parameters, microbial biomass and activity on the field permits the development of an effective strategy for maintaining sustainable soil productivity through the appropriate management of agricultural practices and the better approximation of mineral fertilization. [ABSTRACT FROM AUTHOR]

Mungbean (Vigna radiata L.), one of the most widely grown edible legumes in Asia, plays important roles in the improvement of agricultural cultivation systems as well as human diets. As an understudied crop, however, most mungbean varieties are characterized by unstable yields, poor disease resistance, and unsuitability for mechanical harvesting, thereby leading to a low production income. We, therefore, developed Jilv 20, a new mungbean cultivar with a dull seed coat, by crossing Bao 942-34 with Weilv 9002-341. After 11 years of selection and evaluation, Jilv 20 has shown wide adaptability, early maturity, high yield, halo blight resistance, and suitability for mechanical harvesting. The growth period of Jilv 20 is 79.5 and 66.7 days in the spring and summer sowing seasons, respectively, and the average plant height is approximately 56.4 cm. In this study, average yields were 1737.9, 1532.3, and 2045.1 kg/hm2 in northern spring-sowing, northern summer-sowing, and southern sites, respectively; these values were respectively 27.83%, 28.48%, and 6.96% higher than those of control cultivar Zhonglv 5, which has been popular in past decades because of its wide adaptability. The average protein and starch contents of Jilv 20 seeds were 25.0% and 49.56%, respectively. Further application and extension of Jilv 20 in China should contribute to mungbean production, breeding, and industrial development. [ABSTRACT FROM AUTHOR]

Legume–cereal mixtures are pivotal in yielding a more balanced forage composition compared to the sole crops, due to the well-adjusted equilibrium of carbohydrate and protein. However, great attention is required in choosing the optimal ratio of the components for the mixtures and other agronomic practices (including N fertilization and stage of harvest), because they can sharply change the forage composition and quality. To fill this gap, the present research compared the herbage and silage qualities and key fermentative and nutritional traits of biomasses obtained by: (i) five mixtures (i.e., 100:0; 75:25, 50:50, 25:75, and 0:100) of hybrid barley (Hordeum vulgare L.) and faba bean (Vicia faba var. minor); (ii) two N barley fertilization rates (0 vs. 140 kg N ha−1); and (iii) three stages of harvest (milky, early dough and soft dough). We found that the presence of faba bean improved the forage quality, either herbage or silage, through different mechanisms. In the herbage mixtures, faba bean inclusion increased crude protein (CP), and, when compared to the fertilized barley, also water-soluble sugar (WSCs) concentration, with small effects on the relative feed values (RFV) and total digestible nutrients (TDN). In the silage, the higher the faba bean share in the mixture, the higher the RFV, the non-fibrous carbohydrates, and total and lactic acid concentrations, and the lower the pH and the hemicellulose and cellulose concentrations, particularly at the early and mid stages of harvest. These improvements were likely due to the high ability of faba bean to attain a vegetative status (i.e., low dry matter concentration) by the end of the cropping season, and to improve the organic acid production. Our results suggest that the inclusion of faba bean in a barley-based silage system can widen the window for silage harvesting, thanks to its ability to sustain the fermentation process, despite a lower WSC to CP ratio compared to the unfertilized barley. These results occurred almost irrespectively of the faba bean share in the mixture and barley fertilization. This implies that when designing intercrop mixtures, the effect of crop densities on yield should be considered more than on quality. [ABSTRACT FROM AUTHOR]