IoT and Edge-AI Enabled Autonomous Agri-Robot for Precision Irrigation and Early Plant Disease Diagnosis Using Attention-Guided Lightweight CNN and Fuzzy Logic Control

This paper presents an IoT and edge-AI enabled autonomous agricultural robot that performs early plant disease diagnosis and precision irrigation on a single mobile platform. Unlike earlier automated farming systems that rely on visible-spectrum (RGB) imagery and simple threshold-based watering, the proposed system fuses RGB and nearinfrared (NIR) imagery to compute the Normalised Difference Vegetation Index (NDVI), enabling detection of physiological plant stress several days before visible lesions appear. Leaf images are classified using a lightweight attention-guided convolutional neural network that combines a MobileNetV3 backbone with a Convolutional Block Attention Module (CBAM), allowing the network to focus on lesion-relevant channels and spatial regions while remaining compact enough for real-time inference on an ESP32-S3 edge controller. Irrigation and pesticide-spray decisions are no longer governed by a rigid binary threshold; instead, a Mamdani-type fuzzy inference engine fuses soil moisture, ambient temperature, and the NDVI-derived stress index to compute a proportional, continuously variable actuation signal, reducing both water wastage and false triggering. The robot streams sensor readings, classification results, and actuation logs to a cloud dashboard over Wi-Fi/MQTT so that farmers can monitor crop health and irrigation status remotely and receive real-time alerts. Experimental evaluation on a prototype platform shows that the proposed attention-guided model improves disease-classification accuracy over a baseline CNN, the NDVI-assisted pipeline detects stress earlier than colour-only analysis, and the fuzzy irrigation controller reduces water consumption relative to the binary threshold scheme while maintaining optimal soil-moisture levels. The results indicate that combining multispectral sensing, attention-based lightweight deep learning, and fuzzy control on a single autonomous platform is a practical and scalable route towards sustainable, resource-efficient precision agriculture.