Potato is one of the most important food crops in the world, which is of great significance for sustainable agricultural development. Mechanized planting is the essential technical link in mechanized production, which has an essential component in the potato growing industry. The mechanization of potato planting technology is an effective method of increasing potato yields. A variety of potato planting technologies and machines have been developed around the world. This review presents the research progress and application status of potato planters and planting technology worldwide. It classifies the planting technology into four types: research of materials characteristics for potatoes, soil cultivation, seed potato separation, and zero-speed seeding. The most critical seed potato separation technology was divided into six types according to the structure of the seed metering device. Detailed features have been provided for some typical potato planters and soil cultivation machines. Finally, the developing trend of intelligent planting technology was analyzed, and some suggestions were proposed to promote the development of potato planters.

In order to design a branch-cutting type green pepper harvesting device, we firstly study the whole process of straight knife green pepper cutting to reveal the cutting mechanism and provide theoretical guidance to the design. A finite element model was established for the cutting of pepper branches across the distance, and single-factor and multi-factor finite element simulation tests were conducted on the knife feed angle, tool edge angle, and knife feed speed of the working parts of the pepper cutting and harvesting device. The results of the experiment were analyzed by ANOVA, which showed the different degrees of importance of these factors, and the optimal parameters were obtained by response surface methodology (RSM). With the optimal parameters selected, the predicted maximum cutting force and cutting completion were 803.35 N and 98.58%, respectively, this satisfies the efficiency and economy requirements of agricultural machinery design. In addition, the cutting force of green pepper branches was analyzed and a theoretical mechanical model was developed to help us understand the variation of cutting force numerically. The stress–strain system, high-speed photography system and numerical prediction were innovatively combined to observe and measure the stress and other key state variables in the cutting process in detail, summarize their changing trend, and establish a time-based monitoring and comparison model. The above research results can provide a reference for the design of green pepper branch cutting and harvesting devices, such as direct guidance on the selection of working parameters, materials, etc., and guidance on the operation in actual work.

Reliable detection of small agricultural targets is fundamental to precision crop protection, phenotyping, yield estimation, and robotic intervention. Typical examples include detecting aphids such as Aphis gossypii, whiteflies such as Bemisia tabaci, planthoppers such as Nilaparvata lugens, and other tiny pests on sticky traps or crop canopies for early warning, identifying crop-like weed seedlings for site-specific herbicide spraying, locating early disease lesions for targeted treatment, and detecting young fruits, flowers, or wheat heads for yield estimation and robotic manipulation. Agricultural small-object detection differs from generic small-object detection because target visibility is jointly determined by pixel area, physical size, imaging distance, ground sampling distance, canopy structure, biological similarity, and task-specific intervention requirements. Existing reviews have summarized agricultural object detection or general small-object detection, but they rarely connect agricultural failure modes with detector-level mechanisms and reproducible evaluation practices. This review addresses this gap through a mechanism-oriented synthesis of agricultural small-object detection. First, we revisit the limitations of the COCO-style 32²-pixel threshold and propose an agricultural scale-reporting framework that combines pixel area, physical scale, relative image occupancy, and acquisition geometry. Second, we organize recent methods according to the mechanisms by which they address detail loss, scale shift, occlusion, dense distributions, foreground–background confusion, localization uncertainty, and edge-deployment constraints. Third, we summarize public datasets, quantitative evaluation metrics, reporting checklists, and real-device deployment evidence to support fair and field-oriented comparison. Finally, we identify future directions in multimodal sensing, foundation-model adaptation, label-efficient learning, and hardware-aware optimization. By linking agricultural scene characteristics, detector mechanisms, and evaluation requirements, this review aims to provide a more actionable framework for developing robust small-object detection systems in precision agriculture.

Stem mustard, the main raw material for pickled mustard tuber, is widely planted in Chongqing, China, and is an important local cash crop. Under the working conditions of sticky and wet soil in the Chongqing area, conventional furrow seeding has problems such as soil sticking to the furrow opener, poor mulching effect, etc. In this regard, this paper proposes the use of non-contact, soil-based, pneumatic shot seeding, in which seeds are shot into the soil to a predetermined depth by a high-speed air stream. The diameter of stem mustard seeds was found to be 1.33 mm, with a spherical rate of 95.32% using physical and mechanical properties. The high-speed camera test was used to determine the air pressure at the appropriate sowing depth, and the seed entry process was simulated by EDEM 2021 software, which analysed the movement process of the seed after entering the soil, and the structure of the seeder was designed based on the resulting test data. The structural parameters of the shot seeding device were analysed by a hydrodynamic simulation using Fluent 2022 R1 software and the following results were obtained: an outlet pipe diameter D_C of 2 mm, mixing zone length H of 10 mm, mixing zone inlet diameter D of 15 mm, and steady-state gas flow rate of 80 m/s. Simulation seeding verification was conducted on the final determined structural parameters of the seeding device, and the simulation results showed that the seed velocity could reach 32.3 m/s. In actual experiments, it was found that when the vertical velocity of the seeds was greater than or equal to 26.59 m/s, the seeds could be completely and stably seeded into the soil. Therefore, the designed seeding device can meet the conditions of actual seeding experiments. In conclusion, this research offers a practical guideline for the rapid and precise sowing of stem mustard.

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop.

Based on the cultivation environment and material parameters of Pleurotus pulmonarius sticks, a P. pulmonarius stick cutting machine is designed to solve the problem of directly realizing the cutting of P. pulmonarius sticks for a net-shaped mushroom stick cultivation layer frame. The machine consists of three parts: a traveling trolley, an XYZ three-axis moving mechanism, and a clamping and cutting mechanism. Based on the force analysis of the clamping and cutting mechanism and the P. pulmonarius sticks, the key components were designed and selected. A prototype was constructed, and several cutting tests were conducted, with the cutting speed, number of cutting circles, and depth of the cut as the main factors. The success rate of cutting, the roundness of the cut, and the degree of loss in terms of the cutout substrate, were used as the performance indicators. An optimized design, based on orthogonal testing, was employed to determine the optimal parameter combinations for the cutting process. The optimal parameters were found to be a cutting speed of 376.3 r/min, 5.4 cutting circles, and a cutting depth of 4 mm. Finally, a validation test was carried out on the machine, and the results show that under the same parameter conditions, the success rate of cutting was 86.6%, the roundness of the cut was 0.235, and the degree of loss in terms of the cutout substrate was 0.851%, which is consistent with the optimization results. This study provides a reference, including a practical reference, for the design and development of a machine to realize the cutting of P. pulmonarius sticks for a cultivation layer frame.