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Crop diseases significantly impact crop yields, and promoting specialized control of crop diseases is crucial for ensuring agricultural production stability. Disease identification primarily relies on human visual inspection, which is inefficient, inaccurate, and subjective. This study focused on the plum red spot (Polystigma rubrum), proposing a two-stage detection algorithm based on deep learning and assessing the severity of the disease through lesion coverage rate. The specific contributions are as follows: We utilized the object detection model YOLOv8 to strip leaves to eliminate the influence of complex backgrounds. We used an improved U-Net network to segment leaves and lesions. We combined Dice Loss with Focal Loss to address the poor training performance due to the pixel ratio imbalance between leaves and disease spots. For inconsistencies in the size and shape of leaves and lesions, we utilized ODConv and MSCA so that the model could focus on features at different scales. After verification, the accuracy rate of leaf recognition is 95.3%, and the mIoU, mPA, mPrecision, and mRecall of the leaf disease segmentation model are 90.93%, 95.21%, 95.17%, and 95.21%, respectively. This research provides an effective solution for the detection and severity assessment of plum leaf red spot disease under complex backgrounds.

As the use of triboelectric nanogenerators (TENGs) increases, the generation of related electronic waste has been a major challenge. Therefore, the development of environmentally friendly, biodegradable, and low-cost TENGs must be prioritized. Having discovered that plant proteins, by-products of grain processing, possess excellent triboelectric properties, we explore these properties by evaluating the protein structure. The proteins are recycled to fabricate triboelectric layers, and the triboelectric series according to electrical properties is determined for the first time. Using a special structure design, we construct a plant-protein-enabled biodegradable TENG by integrating a polylactic acid film, which is used as a new type of mulch film to construct a growth-promoting system that generates space electric fields for agriculture. Thus, from the plant protein to the crop, a sustainable recycling loop is implemented. Using bean seedlings as a model to confirm the feasibility of the mulch film, we further use it in the cultivation of greenhouse vegetables. Experimental results demonstrate the applicability of the proposed plant-protein-enabled biodegradable TENG in sustainable agriculture.

High-precision printing of flexible wireless electronics has not been achieved before. Here, the authors leverage a room-temperature direct printing strategy to realize an all-MXene-printed integrated system capable of wireless communication, energy harvesting, and smart sensing.

Abstract Rapid iterations of sensing, energy, and communication technologies transform traditional agriculture into standardized, intensive, and smart modern agriculture. However, the energy supply challenge for the plentiful sensors or other microdevices constraints the extensive application of intelligent technologies in agriculture. Triboelectric nanogenerator (TENG), which efficiently converts mechanical energy into electrical energy through contact electrification and electrostatic induction, is considered a promising way to build next‐generation intelligent energy supply networks. By efficiently harvesting low‐frequency mechanical energy from the agricultural environment, including wind, rain, and water flow energy, TENGs can be a strong contender for distributed power for microdevice networks in smart agriculture. In addition, highly customizable TENGs can be combined with microdevices in agriculture to enable self‐powered agricultural monitoring and production strategy adjustment. By deeply exploring the application potential of TENG in agriculture, it is conducive to further promoting unmanned production, refinement, and intelligence of agricultural production and enhancing agriculture's ability to combat natural risks.