International Journal of Science, Engineering and TechnologyAuthors: Vedarsh Lokhande, Omkar Holkar, Om Gadilkar, Kshitij Nawale, Tirupati Hale, Dr. N.P Bhone
Abstract: Pests and diseases on crops are a crucial challenge impacting agriculture productivity, causing global losses up to 20%-40% annually [1]. In this paper, we propose AI and IoT Based Pest Detector (IPD), an IoT-AI integrated system capable of real-time crop infestation detection and intelligent advisement generation. It integrates a hardware node based on ESP32 (equipped with various environmental sensors: DHT22(temperature/humidity), capacitive soil moisture sensor, leaf wetness sensor, MQ-135 gas sensor, KY-038 sound sensor, and OV2640 camera module) with a pipeline that blends deep learning for image analysis and large language model (LLM). The training of a custom Convolutional Neural Network (CNN) to recognize 38 different classes of plant diseases based on the PlantVillage dataset [2] shows an accuracy of 97.07% on training data and 95.08% on validation data over 10 epochs. When a leaf image is uploaded, an inference pipeline first classifies if the image falls in the 38 recognized categories; if so, it applies the trained CNN for classification and a Gemini API [12] to generate an advisory (infestation severity, organic, chemical treatment, prevention strategies and helpful agricultural numbers); if not, Gemini would give out a direct AI analysis and suggestion. All the detected information are presented on a web dashboard made by Streamlit and simultaneously sent to a local OLED screen. Our proposed system is an all-in-one and cost-effective solution, expandable to both small farms and precision farming contexts.
