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Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems.

Machine harvesting blueberry (Vaccinium sp.) alleviates labor costs and shortages but can reduce fruit quality. Installation of softer catching surfaces inside modified over-the-row harvesters (modified OTR) and adjusting harvest intervals may improve fruit quality and packout. The objective of this study was to determine the effect of harvest interval on fruit quality of fresh market northern highbush blueberry (Vaccinium corymbosum L.) harvested using a modified OTR. ‘Liberty’ blueberry bushes were harvested by hand or using a modified OTR at 3-, 10-, and 14-day intervals in 2019 and at 7-, 11-, and 14-day intervals in 2020. Hand-harvested ‘Liberty’ had greater packout and firmness than machine-harvested fruit. Machine harvesting at the 3-day interval in 2019, and the 14-day interval in 2020 reduced packout from 70–80% to 60% and 54%, respectively. In 2019, machine harvesting at a 3-day interval overall resulted in fruit with greater firmness, higher titratable acidity (TA), and lower total soluble solids (TSS) and SS/TA, compared to other harvest intervals. In 2020, the 7-day machine-harvest interval had a greater TA and lower TSS/TA, compared to the 11- and 14-day intervals. Overall, modified OTR machine-harvest intervals can be extended to 10–11 days for fresh market northern highbush cultivars such as ‘Liberty’ grown in northwest Washington.

Grain price differences due to protein content can have economic effects on the farm as well as environmental effects when alternative protein sources are imported. Grain protein variability can vary from year to year due to environmental factors and can be addressed by site-specific management practices. Alternatively, it can be addressed at harvest time by selective harvest. Agricultural autonomous robots can accurately follow alternative harvesting routes that are subject to grain quality maps, making them suitable choices for selective harvest. This study addresses therefore the potential revenue of selective harvest performed by the route planner of an autonomous field robot. The harvest capacity and potential economic revenues of selective harvest in a Danish context were studied for a set of 20 winter wheat fields with four hypothetical scenarios. The results showed significant differences in harvest capacity between conventional and selective harvest. Even though in some scenarios selective harvest did not require notable additional harvest times, the cost–benefit analysis showed small economic returns of up to 46 DKK ha−1 for the best scenarios, and for most cases losses up to 464 DKK ha−1. Additionally, the location of the high protein content areas has great influence on the profitability of selective harvest.

PurposePath planning and coordination algorithms are needed for deploying multiple unmanned ground vehicles (UGVs) on a smart farm. These algorithms differ in performance because of constraints such as the number of robots, obstacle configuration, and environment density. Therefore, this paper aims to select the most robust path planning and coordination algorithm by evaluation under the constraints of smart farms.MethodsIn this study, we evaluated three path planning and coordination algorithms: (1) conflict-based search (CBS), (2) enhanced CBS (ECBS), and (3) push and rotate (PAR) algorithms. The evaluation was performed in an environment mimicking an actual smart farm. We obtained the number of collisions, success rates, and total travel distance (TTD) data. For statistical analysis, a t-test was performed on TTD data.ResultsThe statistical analysis of the TTD confirmed no difference between the performance of the three algorithms. The ECBS and PAR algorithms exhibited the same 100% success rate for robot systems with fewer than 10 robots. Furthermore, the PAR algorithm exhibited a robust performance in all robot systems with at most 20 robots.ConclusionsWe evaluated three conventional path planning and coordination algorithms for a multi-UGV system. The evaluation was performed in an environment that mimicked an actual smart farm. Although there is no difference in the path generation performance of all algorithms, the results revealed that the PAR algorithm was the most robust given the constraints in a smart farm.

Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

In recent years, the fruit industry has become an important part of agricultural development, and fruit harvesting is a key stage in the production process. However, picking fruits during the harvest season is always a major challenge. In order to solve the challenges of time-consuming, costly, and inefficient fruit picking, researchers have conducted a lot of studies on automatic fruit picking equipment. Existing picking technologies still require further research and development to improve efficiency and reduce fruit damage. Aiming at the efficient and non-destructive picking of fruits, this paper reviews machine vision and mechanical fruit picking technology and the current research status, including the current application status, equipment structure, working principle, picking process, and experimental results. As a promising tool, machine vision technology has been widely researched and applied due to its low hardware cost and rich visual information. With the development of science and technology, automated fruit picking technology integrates information technology, integrates automatic perception, transmission, control, and operation, etc., saves manpower costs, and continuously promotes the development of modern agriculture in the direction of refinement of equipment technology, automation, and intelligence. Finally, the challenges faced by automated fruit picking are discussed, and future development is looked forward to with a view to contributing to its sustainable development.

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

Automated crop monitoring using image analysis is commonly used in horticulture. Image-processing technologies have been used in several studies to monitor growth, determine harvest time, and estimate yield. However, accurate monitoring of flowers and fruits in addition to tracking their movements is difficult because of their location on an individual plant among a cluster of plants. In this study, an automated clip-type Internet of Things (IoT) camera-based growth monitoring and harvest date prediction system was proposed and designed for tomato cultivation. Multiple clip-type IoT cameras were installed on trusses inside a greenhouse, and the growth of tomato flowers and fruits was monitored using deep learning-based blooming flower and immature fruit detection. In addition, the harvest date was calculated using these data and temperatures inside the greenhouse. Our system was tested over three months. Harvest dates measured using our system were comparable with the data manually recorded. These results suggest that the system could accurately detect anthesis, number of immature fruits, and predict the harvest date within an error range of ±2.03 days in tomato plants. This system can be used to support crop growth management in greenhouses.

In this paper, we presents a case study involving the implementation experience and a methodological framework through a comprehensive comparative analysis of the YOLOX and YOLOv12 object detection models for agricultural automation systems deployed in the Jetson AGX Orin edge computing platform. We examined the architectural differences between the models and their impact on detection capabilities in data-imbalanced potato-harvesting environments. Both models were trained on identical datasets with images capturing potatoes, soil clods, and stones, and their performances were evaluated through 30 independent trials under controlled conditions. Statistical analysis confirmed that YOLOX achieved a significantly higher throughput (107 vs. 45 FPS, p < 0.01) and superior energy efficiency (0.58 vs. 0.75 J/frame) than YOLOv12, meeting real-time processing requirements for agricultural automation. Although both models achieved an equivalent overall detection accuracy (F1-score, 0.97), YOLOv12 demonstrated specialized capabilities for challenging classes, achieving 42% higher recall for underrepresented soil clod objects (0.725 vs. 0.512, p < 0.01) and superior precision for small objects (0–3000 pixels). Architectural analysis identified a YOLOv12 residual efficient layer aggregation network backbone and area attention mechanism as key enablers of balanced precision–recall characteristics, which were particularly valuable for addressing agricultural data imbalance. However, NVIDIA Nsight profiling revealed implementation inefficiencies in the YOLOv12 multiprocess architecture, which prevented the theoretical advantages from being fully realized in edge computing environments. These findings provide empirically grounded guidelines for model selection in agricultural automation systems, highlighting the critical interplay between architectural design, implementation efficiency, and application-specific requirements.