The release of nanoparticles into the environment is inevitable, which has raised global environmental concern. Melatonin is involved in various stress responses in plants. The present study investigated the effects of melatonin on photosynthetic carbon (C) assimilation and plant growth in nano-ZnO stressed plants. It was found that melatonin improved the photosynthetic C assimilation in nano-ZnO stressed wheat plants, mainly due to the enhanced photosynthetic energy transport efficiency, higher chlorophyll concentration and higher activities of Rubisco and ATPases. In addition, melatonin enhanced the activities of antioxidant enzymes to protect the photosynthetic electron transport system in wheat leaves against the oxidative burst caused by nano-ZnO stress. These results suggest that melatonin could improve the tolerance of wheat plants to nano-ZnO stress.

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.

Effects of daily light integral on tomato (Solanum Lycopersicon L.) grafting and quality in a controlled environment

Water plays a very important role in the growth of tomato (Solanum lycopersicum L.), and how to detect the water status of tomato is the key to precise irrigation. The objective of this study is to detect the water status of tomato by fusing RGB, NIR and depth image information through deep learning. Five irrigation levels were set to cultivate tomatoes in different water states, with irrigation amounts of 150%, 125%, 100%, 75%, and 50% of reference evapotranspiration calculated by a modified Penman-Monteith equation, respectively. The water status of tomatoes was divided into five categories: severely irrigated deficit, slightly irrigated deficit, moderately irrigated, slightly over-irrigated, and severely over-irrigated. RGB images, depth images and NIR images of the upper part of the tomato plant were taken as data sets. The data sets were used to train and test the tomato water status detection models built with single-mode and multimodal deep learning networks, respectively. In the single-mode deep learning network, two CNNs, VGG-16 and Resnet-50, were trained on a single RGB image, a depth image, or a NIR image for a total of six cases. In the multimodal deep learning network, two or more of the RGB images, depth images and NIR images were trained with VGG-16 or Resnet-50, respectively, for a total of 20 combinations. Results showed that the accuracy of tomato water status detection based on single-mode deep learning ranged from 88.97% to 93.09%, while the accuracy of tomato water status detection based on multimodal deep learning ranged from 93.09% to 99.18%. The multimodal deep learning significantly outperformed the single-modal deep learning. The tomato water status detection model built using a multimodal deep learning network with ResNet-50 for RGB images and VGG-16 for depth and NIR images was optimal. This study provides a novel method for non-destructive detection of water status of tomato and gives a reference for precise irrigation management.

To mitigate the high stubble rates (root residue rates) and plant damage associated with the current mechanized harvesting of Shanghai Green (Brassica rapa subsp. chinensis), this study developed and optimized a novel soil loosening and root lifting device. A theoretical dynamic model was first established to analyze the device’s operational principles. Subsequently, a coupled multi-body dynamics and discrete element method (RecurDyn-EDEM) model was established to simulate the complex interactions between the device, soil, and plant roots. Response surface methodology was employed to optimize key operational parameters: walking speed, loosening depth, and vibration frequency. The simulation-based optimization was validated by field tests. The optimal parameters were identified as a walking speed of 0.137 m/s, a loosening depth of 34.5 mm, and a vibration frequency of 1.34 Hz, under which the Shanghai Green pulling force was 35.41 N, yielding optimal extraction performance. Field tests conducted under these optimal conditions demonstrated excellent performance, achieving a qualified plant posture rate of 87.5% and a low damage rate of 7.5%. This research provides a robust design and validated operational parameters, offering significant technical support for the development of low-loss harvesting equipment for leafy vegetables.