Bin Yan

China is the world’s third-largest sugarcane producer. When mechanically harvested sugarcane enters the sugar mill, impurity rate detection is required. However, due to the piling up of sugarcane, significant errors may occur in the detection results. Therefore, this research addresses the issue of low accuracy in machine vision detection due to the dense stacking of sugarcane. An innovative graded device was developed, featuring a three-stage progressive geometric constraint system with roller-belt gaps of 100 mm, 45 mm, and 33 mm, alongside differential traction with speed ratios of 3:1, 4:1, and 5:1. Utilizing the normal distribution characteristic for the diameter of 500 sugarcane stalks, the gap parameters were refined through a dynamic stiffness model. Through power validation and multi-factor orthogonal experiments, the study uncovered the interactive influences of sugarcane weight, primary conveyor belt speed, and speed ratio on the single-layer rate and area ratio. Our findings indicate that sugarcane weight is the primary determinant of the material’s single-layer rate, while the speed ratio is crucial for managing sugarcane distribution density, more so than the primary conveyor belt speed. Notably, increasing the speed ratio from 3:1 to 5:1 results in a decrease in area ratio from 26.8% to 22.0%. After utilizing the graded differential device, the average accuracy of machine vision detection achieved 94.90%, with only two misidentifications on average. In comparison to the control group, detection accuracy improved by 26.93%, misidentifications dropped by about 81.80%, and detection speed was recorded at 55.5 ms. These outcomes confirm that the device not only enhances detection accuracy but also significantly lowers the misidentification rate, thereby creating a stable, clear, and efficient detection environment.

In intensive breeding systems, goats reared for meat production are often housed in group pens at high stocking densities. This study aimed to investigate the correlation between expressed behaviours and stocking density, and to compare the time budget of these confined goats with that of pasture-based goats. A detailed ethogram of 19 mutually exclusive behavioural activities was developed. Behavioural observations were conducted continuously over 72 h on group pens selected for their variation in stocking density and homogeneity in breed, age, body condition and acclimation period since arrival. Using the scan-sampling method (96 scans per goat daily), data were collected from 42 goats. The time budget, expressed as the mean frequency (%) ± standard deviation for each behavioural activity, was calculated. The associations between time budget and stocking density were assessed via bivariate analysis, with the strength and direction of relationships quantified using Pearson’s correlation coefficient (r). Results indicated that self-grooming and Bipedal stance/Climbing were positively correlated with increased space allowance (i.e., lower stocking density), suggesting their potential utility as positive welfare indicators for housed fattening goats in group pens. Furthermore, the time budget differed notably from pasture-based patterns, primarily characterized by resting (53.09% ± 2.72%), eating (16.05% ± 2.88%), and moving (2.30% ± 0.75%).

To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on previously established cultivation patterns and agronomic requirements. The structural configuration and operational principles of the platform’s power chassis are elucidated, with specific emphasis on the development of the traction power system and modular operational systems. Theoretical and experimental analyses of steering parameters, stability, and field performance were conducted. Finite element simulation analysis of the frame revealed that under full-load conditions, the equivalent elastic strains during descent and ascent phases were 0.000317 and 0.00125, respectively. Maximum equivalent stresses (48.27 MPa and 231.6 MPa for descent and ascent, respectively) were localized at the beam–plate junctions, while peak deformations of 1.14 mm (descent) and 4.31 mm (ascent) occurred at mid-frame and posterior–mid regions, respectively. Field validation demonstrated operational velocities of 0.16–1.77 m/s (forward) and 0.11–0.28 m/s (reverse), with a maximum gradability of 20°. The platform exhibited multifunctional capabilities including weeding, spraying, fertilization, flower induction, harvesting, and transportation, demonstrating its potential to fulfill the operational requirements for pineapple field management. Simultaneously, the overall work efficiency is increased by more than 50%, compared to manual labor.

Induction in pineapples requires the targeted delivery of specific chemical solutions into the plant’s central core to enable batch management, a task currently reliant on manual operation. This study addressed this challenge by analyzing the physical characteristics of pineapple plants and establishing a perception-based mathematical model for core position localization. An integrated hardware–software system was developed, complemented by a human–machine interface for real-time operational monitoring. Comprehensive experiments were conducted to evaluate the spraying accuracy, nozzle response time, and prototype performance. The results demonstrate that the actuation system—comprising solenoid valves, pumps, and flowmeters—achieved an average spraying error of 2.72%. The average nozzle opening/closing time was 0.111 s; with a standard operating speed of 0.5 m/s, a delay compensation distance of 55.5 mm was implemented. In human–machine comparative trials, the automated system outperformed manual spraying in both efficiency and stability, with average errors of 7.1% and 6.4%, respectively. The system reduced chemical usage by over 67,500 mL per hectare while maintaining a miss-spray rate of 5–6%. Both two-tailed tests revealed extremely significant differences (p < 0.001). These findings confirm that the developed solution meets the operational requirements for pineapple floral induction, offering significant improvements in precision and resource efficiency.

Cassava is one of the world’s staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization.

Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches.

Sugarcane is an important economic crop in tropical and subtropical regions. Presawing planting is an important method for achieving automated and precise planting with sugarcane planting machines. The sawing process is a key stage in planting management, affecting not only the germination and survival rates of sugarcane, but also reflecting the mechanical performance of sawing. To reduce the peak sawing force and enhance the sawing surface quality of sugarcane seedlings, this study utilized a central composite experimental design method. Single-factor and multi-factor experiments were conducted with a specially designed sugarcane stalk sawing experimental rig to investigate the impact of factors such as the stalk diameter feeding speed, and sawing speed on the peak sawing force and sawing surface quality. Upon being developed and validated, multivariate mathematical regression models elucidated the relationships among these factors. The experimental results showed that the order of influence of each factor on the peak sawing force was the stalk diameter, feed speed, and sawing speed, while for the sawing surface quality, the sequence was the sawing speed, stalk diameter, and feed speed. Correspondingly, the determination coefficients for the peak sawing force and sawing surface quality prediction models were 0.9708 and 0.9675. With a maximum error of 7.6% for the peak sawing force and an average relative error of 7.1%, and a maximum error of 3.5% for the sawing surface quality and an average relative error of 2.83%, the calculated results from the regression models were in good agreement with the experimental findings. This indicates that the models are capable of quickly and accurately predicting the peak sawing force and sawing surface quality of sugarcane stalks under different conditions. The research findings provide valuable insights for the development and optimization of sugarcane stalk presawing equipment and related experimental studies.