Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R²) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R² = 0.85 and R² = 0.77, followed by STI, R² = 0.82 and R² = 0.80 and NDRI, R² = 0.69 and R² = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region.

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.

Soybean plants cultivated using mulched drip irrigation planting technology have the following characteristics during the harvest period: green stems and leaves, and a high straw/grain ratio. Moreover, the threshing device of a soybean combine harvester is difficult to adapt to, resulting in an increase in the accumulation and unevenness of the threshed mixture. This leads to an increase in impurity content and the loss rate. We conducted a single-factor experiment on a self-developed longitudinal/axial-flow soybean threshing and separation test bench, employing drum speed, feeding rate, and threshing clearance as experimental factors. The influence of the soybean threshing and separation device’s working parameters on the distribution and uniformity of the threshed mixture in the axial and radial directions of the drum was explored through experiments. The results showed that the mass of the threshed mixture and soybean seeds showed a trend of first rapidly increasing and then slowly decreasing in the axial direction of the drum. Additionally, the mass showed a distribution feature of large values on both sides and small values in the middle in the radial direction. A lower drum speed, greater threshing clearance, and a smaller feeding rate make the radial distribution of a threshed mixture more uniform. Based on the combination of the crushing rate and unthreshed rate, the optimal working parameter combination was determined to be as follows: a drum speed of 500 r/min, a feeding rate of 6 kg/s, and a threshing clearance of 25 mm. The findings of this research offer valuable insights for the structural optimization and design enhancement of threshing and cleaning mechanisms within soybean combine harvesters.

In order to solve the problems of bottom pod leakage and soil removal by header, a soybean header profiling system was designed in this paper. The cutter height off-ground detection device was installed on both sides of the header, and the cutter distance from the ground was represented by the angle sensor turning when the profiling wheel met the rolling ground. The hydraulic electromagnetic reversing valve was installed so that the profiling system could automatically control the lifting of the header, the unilateral power of the solenoid valve was 0.15 s, and the height of the cutter from the ground was changed by 10 mm. The height of the cutter off the ground was set to 80 mm, and the adjustment range of the soybean header profiling system was 45–125 mm. The test results showed that the maximum absolute error of the cutter off the ground height detection device was 5.98 mm, the minimum absolute error was 1.00 mm, and the relative error was 0.038. The cutter height adjustment device was powered for 0.15 s, and the average adjustment distance was 11.158 mm. The soybean header profiling system did not shovel soil during field harvest, and the stubble height of 85% of soybean plants was less than 10 mm from the set height after harvest. The results showed that the soybean header profiling system could effectively adjust the cutter height from the ground so that the cutter height from the ground was kept at 80 mm. This study could provide a reference for the intelligent design of soybean harvesters.

Inadequate parameter design of the cleaning device in soybean combine harvesters leads to elevated levels of machine harvesting losses and impurity rates. To provide fundamental data for the optimization of structural parameters of soybean cleaning sieves, it is of great significance to study the collision and bouncing characteristics of soybeans on the cleaning sieve surface and the impact of parameters on the coefficient of restitution (COR). The current study designed a collision platform, using soybeans at the harvest stage as the research subject. The experimental factors included drop height, wall inclination angle, wall movement speed, and wall material. Through single-factor experiments and orthogonal experiments, the effects of different collision parameters on the rebound trajectory and COR of soybeans were investigated. This study focuses on soybeans at the harvest stage as the test subjects. Experiments were conducted on a collision platform and recorded with a high-speed camera to capture the three-dimensional motion trajectories of the soybeans using the principle of specular reflection. Through single-factor experiments, the jumping characteristics of the soybeans on sieve surfaces with different motion characteristics were analyzed. The impact of drop height (400–650 mm), wall inclination angle (8–13°), wall movement speed (0.6–1.1 m/s), and wall material (stainless steel plates and polyurethane plates) on the coefficient of restitution (COR) was calculated and clarified. Multi-factor orthogonal experiments were conducted to determine the significance order of the different factors affecting the COR. Three-dimensional models of the soybeans and the collision platform were constructed using SolidWorks software, and the collision between the soybeans and the cleaning wall was simulated using EDEM software. The micro-forces and energy transfer during the soybean collision were analyzed. The results indicated that the COR of soybeans decreases as the drop height increases, but increases with wall inclination angle and wall movement speed. Additionally, the COR is higher when the soybeans collide with stainless steel plates compared to polyurethane plates. The order of influence of the four factors on the COR were: wall material > wall inclination angle > wall speed > drop height. This study provides important reference value for the efficient and low-loss design of cleaning devices.

Cumin (Cuminum cyminum L.) is a globally important spice crop, particularly significant in Xinjiang, China, where it is extensively cultivated in cotton–cumin intercropping systems. This review concentrates on the serious bottleneck hindering the development of the cumin industry: the low level of harvesting mechanization. Traditional manual harvesting methods are labor-intensive, inefficient, and result in high yield losses. This paper fully explores the prospects and challenges of mechanizing cumin harvesting in accordance with the particular biological characteristics of cumin plants and the complexity of intercropping systems. We review the current status of research in the following domains: (1) cumin biological traits and intercropping models; (2) grain loss and stalk damage patterns in stripper harvesting of similar crops; (3) factors influencing root–soil interaction during mechanical extraction; (4) uprooting–conveying harvesting techniques and row division/plant singulation methods applicable to root and tuber crops; and (5) cumin-threshing and -cleaning technologies. This review highlights the inadequacy of current grain-harvesting machinery for cumin and underscores the urgent need for specialized, low-loss harvesting technologies tailored to cumin’s delicate nature and intercropping context. Finally, we propose future research directions to overcome these mechanization challenges and promote the sustainable development of the cumin industry.