AgriSage: Android‐Based Application for Empowering Farmers With E‐Commerce and AI‐Driven Disease Detection - Shabeena Naveed; Mujeeb Ur Rehman; Mumtaz Ali Shah; Shahid Sultan; Zafar Ullah Khan; Syed Zarak Shah; Mansoor Iqbal; Muhammad Ahsan Amjed (Engineering Reports)
ABSTRACT Agriculture faces critical challenges such as timely disease detection, fragmented market access, and limited use of real‐time technology in the field. To address these issues, we developed AgriSage, an Android‐based intelligent mobile application that integrates artificial intelligence, weather forecasts, and governmental scheme updates to support farmers, sellers, customers, and policymakers. The application incorporates two optimized deep learning models designed for on‐device deployment. The first model, based on MobileNetV2, performs binary classification to detect the presence of plants in images. It achieved a precision, recall, and F1‐score of 1.00 for both classes, indicating perfect classification performance on the test set. On‐device inference testing of the converted TensorFlow Lite model resulted in an average prediction time of approximately 3736.44 ms per image when evaluated through the validation pipeline. Another deep learning model, that is, a convolutional neural network designed for disease classification, was trained on the PlantVillage dataset across 38 classes. It achieved a macro average F1‐score of 0.8207 and a weighted average F1‐score of 0.8703. The optimized TensorFlow Lite version demonstrated an average inference time of 35.6 ms per image, confirming its suitability for real‐time, on‐device deployment. AgriSage delivers a robust and scalable platform integrating AI‐powered crop monitoring and disease detection. It also provides real‐time agricultural support services, contributing to improved decision‐making and promoting sustainable farming practices.