Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry.

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems.

To address the problems of low crushing efficiency and uneven distribution in traditional straw crushing and returning machines for cotton stalk return operations in Xinjiang, a vertical straw crushing and returning machine with large and small dual-blade discs was designed, adapted to Xinjiang’s cotton planting model. The machine employs a differentiated configuration of large and small blade discs corresponding to four and two rows of cotton stalks, respectively, effectively reducing tool workload while significantly improving operational efficiency. A simulation model of the crushing and returning machine was developed using the discrete element method (DEM), and a flexible cotton stalk model was established to systematically investigate the effects of machine forward speed, crushing blade rotational speed, and knife tip-to-ground clearance on operational performance. Single-factor simulation experiments were conducted using crushing qualification rate and broken stalk drop rate as evaluation indicators. Subsequently, a multi-factor orthogonal field experiment was designed with Design-Expert software (13.0.1.0, Stat-Ease Inc, Minneapolis, MN, USA). The optimal working parameters were determined to be machine forward speed of 3.5 m/s, crushing blade shaft speed of 1500 r/min, and blade tip ground clearance of 60 mm. Verification tests demonstrated that under these optimal parameters, the straw crushing qualification rate reached 95.9% with a broken stalk drop rate of 15.5%. The relative errors were less than 5% compared to theoretical optimization values, confirming the reliability of parameter optimization. This study provides valuable references for the design optimization and engineering application of straw return machinery.

To solve the problems associated with the poor harvesting ability of existing sunflower harvester cutting tables, and high seed drop rates, we designed a sunflower cutting table that can greatly improve the operational performance of sunflower combine harvesters. In this paper, we introduce the structure and principle of the whole machine and select the key parameters of the cutting table with the goal of adapting to a variety of planting modes in Xinjiang, China. Since the harvesting of sunflower in the wrong row easily causes the sunflower stalks to break, ADAMS (Version: 2020) simulation experiments were carried out to investigate the effects of the forward speed of the machine, the height of the sunflower insertion disk, and the angle of inclination of the harvest divider on the offset angle of the sunflower. With the goal of reducing the offset angle of the stalks in the forward direction of the harvest divider and reducing the size of the cutting table, the harvest divider inclination angle was chosen to be 45°; by using Design-Expert V13.0.15 software, a three-factor, three-level field test was carried out to determine the optimal parameter combinations that resulted in the minimum seed loss rate and the maximum success rate of the disk picking. Moreover, a validation test was conducted. The results show that when the forward speed is set to 0.62 m/s, the lifting speed of the pick-up disk device is set to 0.42 m/s, and the height of the inserted disk is set to 1000 mm, the relative errors between the theoretical values of the disk-picking success rate of the cutting table and the seed loss rate and the field test values are 6.5% and 1.3%, respectively. The results of the present study can provide a reference for improving the performance of sunflower harvester cutting tables and for the mechanical harvesting of inserted disk sunflowers.

As a vital oil and cereal crop in China, soybean requires efficient and low-loss harvesting to ensure food security and sustainable agricultural development. However, pod-shattering losses during soybean harvesting in Xinjiang remain severe due to low pod moisture content and poor mechanical strength, while existing studies lack a systematic analysis of the interaction mechanism between reeling devices and pods. The current research on soybean harvester headers predominantly focuses on conventional rigid designs, with limited exploration of flexible reel mechanisms and their biomechanical interactions with soybean pods. To address this, this study proposes an optimization method for low-loss harvesting technology based on mechanical-crop interaction mechanisms, integrating dynamic simulation, contact mechanics theory, and field experiments. Texture analyzer tests revealed pod-shattering force characteristics under different compression directions, showing that vertical compression exhibited the highest shattering risk with an average force of 14.3271 N. A collision model between the spring tooth and pods was established based on Hertz contact theory, demonstrating that reducing the elastic modulus of the spring tooth and increasing the contact area significantly minimized mechanical damage. Simulation verified that the PVC-nylon spring tooth reduced the maximum equivalent stress on pods by 90.3%. Furthermore, the trajectory analysis of spring-tooth tips indicated that effective pod-reeling requires a reel speed ratio (Δ) exceeding 1.0. Field tests with a square flexible spring tooth showed that the optimized reel reduced header loss to 1.371%, a significant improvement over conventional rigid teeth. This study provides theoretical and technical foundations for developing low-loss soybean harvesting equipment. Future work should explore multi-parameter collaborative optimization to enhance adaptability in complex field conditions.

To address the low monitoring accuracy of traditional methods caused by the complex composition and severe signal overlapping of threshed materials during soybean threshing, this study proposes a high-precision impact signal classification system based on edge perception and a hybrid deep learning architecture, serving as a foundational step for threshing loss monitoring. At the hardware level, a high-speed parallel sensing system was developed to achieve continuous acquisition and high-fidelity mapping of transient impact signals. At the algorithmic level, a CNN–Transformer hybrid network was constructed to effectively extract local signal features and capture long-term temporal dependencies, successfully decoupling complex collision dynamics. Bench tests demonstrate that the hybrid model achieves a comprehensive classification accuracy of 97.36% and F1-scores above 0.96 for soybean grains, stems, and pods, significantly outperforming single networks. Furthermore, feature visualization confirms that the model effectively extracted features strongly correlated with the intrinsic impact dynamics of different materials rather than simply fitting environmental noise. This study provides a highly robust algorithm foundation and bench-level engineering reference for the intelligent classification of soybean harvesting mixtures, laying the groundwork for actual loss estimation under real field conditions.