Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

To address the limitations of current cabbage harvesters in China, which are often designed for a single variety and lack adaptability to different cabbage varieties, we developed an electric side-mounted cabbage harvester suitable for field operations in the Jiangsu region of China. This design is informed by the statistical analysis of the physical and agronomic parameters of major cabbage varieties. The harvester consists of key components, including an extraction device, a leaf-stripping device, a clamping and conveying device, and a root-cutting device. Powered by a 120 Ah direct current (DC) power source, it is capable of performing cabbage extraction, feeding, clamping, conveying, root cutting, and boxing in a single operation for three hours. Through theoretical analysis of the key components, specific parameters were determined, and field tests were conducted to verify the design. The results of the field experiments indicate that all components of the cabbage harvester operated effectively. Optimal performance was observed when the extraction roller speed was set between 100 and 110 RPM, the conveyor belt speed at 60 RPM, and the cutter speed between 160 and 220 RPM, resulting in a low cabbage harvest loss rate. The harvest loss rates from the three experiments were 11.3%, 13.3%, and 12%, respectively, which meets the mechanical harvesting requirements for cabbage.

A dynamic management strategy for water and fertilizer application based on morphological characteristics was developed to enhance water use efficiency (WUE) and fruit yield in greenhouse-cultivated tomato (Solanum lycopersicum L.). Multivariate regression analysis was employed to determine the baseline water and fertilizer requirements and to evaluate the effects of varying irrigation and fertilization regimes on fruit yield and WUE. A coupled irrigation–fertilization experiment was conducted, and regression models were established to describe the changes in stem diameter and plant height under these regimes. These models were validated experimentally. The results showed that irrigation significantly influenced both tomato fruit yield and WUE, while fertilization significantly impacted yield, but not WUE. No interactive effects between irrigation and fertilization were observed for either parameter. Stem diameter and plant height were positively correlated with the irrigation and fertilization levels. The proposed dynamic management strategy improved fruit yield by 6.9% and 14.7% under the basic and well-irrigated/fertilized conditions, respectively, compared to that of the fixed regime. Furthermore, model implementation increased WUE by 6.93% and 43.17% and improved the economic benefits by 4.9% and 20.6% under the respective conditions. This provides a practical and effective tool for optimizing water and fertilizer management in greenhouse tomato production, contributing to resource-efficient and high-yield farming practices.