Precision agriculture needs better pest monitoring. Current traps send many images to the cloud. This uses much energy and needs human review. The paper presents a smart trap that runs deep learning on the node. The trap uses a Raspberry Pi and an Intel Neural Compute Stick. It captures images inside pheromone traps. I...
Rice is significantly impacted by its disease leading to loss of rice field yield impacting global food security and thereby effective real-time monitoring solutions are required. This proposed study created a deep learning-based framework for rice disease detection. The proposed framework integrates a patch-based dise...
Accurate and efficient rice-pest detection is essential for field scouting, early warning and precision control. This study presents a lightweight YOLOv11n-based framework for accurate edge deployment. It integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-Io...
Horticultural production plays a vital role in sustaining dietary quality worldwide, yet its efficiency is threatened by insect pest and diseases infestation. Pest damage is becoming more severe as climate variability alters pest behavior and distribution, while insufficient monitoring and reliance on broad-scale, non-...
This review synthesises AI techniques, IoT architectures, and edge-intelligence strategies within a unified framework, highlighting their combined potential and identifying future directions for robust, automated pest detection.
Drones are used to monitor the research status of rice field diseases and pests, the research progress of scholars are summarized, and the differences of the YOLO series, Convolutional Neural Network, Convolutional Neural Network, U-shape Network, and other models in rice disease and pest identification are summarized.
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