Plant Pathology Identification Using Digital Imaging

Abstract

Abstract: This study presents an innovative system for identifying crop diseases using a deep learning approach based on the Mobile Net architecture. Designed for efficiency and lightweight performance, Mobile Net enables accurate disease detection from leaf images while being highly suitable for deployment on mobile devices. The system incorporates a user-friendly graphical interface and a dedicated mobile application, allowing farmers to upload leaf images directly from their smartphones and receive instant diagnoses along with recommended treatments. Trained on the Plant Village dataset, the model is optimized for identifying diseases affecting five major crops: corn, apple, sugarcane, wheat, and grapes. By surpassing the limitations of traditional methods such as K-means clustering and SVM, the proposed system offers higher accuracy, faster processing, and real-time accessibility. This solution aims to minimize crop losses, improve agricultural productivity, and empower farmers with a portable and practical tool for effective crop management.

Code and data availability

The paper describes a MobileNet-based plant disease classification system trained on a custom dataset (and mentions PlantVillage), but provides no public dataset deposit, no author code/model release, and no availability statements or URLs for any paper-specific asset. PlantVillage is a cited prior dataset, not a paper

No evidence-backed public reproduction asset is currently recorded.