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.

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.

In view of the lack of research on the extrusion of pineapple caused by the overall stress response of pineapple at the present stage of pineapple automatic harvesting, the finite element model of pineapples can be studied by constructing such a model. At present, there is still a lack of research on the mechanical properties of the pineapple stem. In this research, the mechanical properties of a pineapple stem were determined by a three-point bending test, a compression test, and a theoretical calculation. Based on the cohesive zone model (CZM), the relevant parameters of the pineapple fruit–stem junction were determined by the fracture test. The overall finite element model of pineapple was established, and then the verification test of the finite element model was carried out. In the validation test, the correlation between sample size parameters and results was analyzed, and the validity of the test sample selection was demonstrated. By substituting the simulation results into the derivation formula, the maximum traction strength and maximum displacement error values were calculated to be 4.3% and 2.9%, respectively, which verified the accuracy of the cohesive zone model parameters. By comparing the maximum load force and load displacement of the load point in the test and simulation, the error of the load force at the fracture point relative to the average value was 3.6%, and the error of the load displacement relative to the average value was 1.4%. The numerical results showed that the model reflected the accuracy of the process of pineapple plant fracture. This study provides a reliable finite element model for future research on pineapple automatic harvesting.

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.