PlantifyAI: A Novel Convolutional Neural Network Based Mobile Application for Efficient Crop Disease Detection and Treatment

Abstract

Crop diseases are a major threat to human food security. Around the world, more than 80% of agricultural production is generated by farmers, and over 50% of their yield is lost due to pests and pathogens, leading to mass disruption in food supply and a large number of hungry people. Identifying a disease correctly is a crucial first step for efficient treatment but remains difficult in many parts of the world due to limited access to agricultural experts and professional infrastructure. The purpose of this research was to create a free, easy-to-use, and widely accessible mobile application that efficiently and accurately, diagnoses 26 diseases of 14 crop species. Furthermore, this application provides treatment steps, common symptoms, and access to recommended curing products for each disease. The real-time crop disease diagnosis is based on a convolutional neural network (CNN) that was trained, validated, and tested on a dataset of 87,860 leaf images split into 38 classes. To design an optimal CNN, 16 different CNNs were designed and tested. MobileNetV2 using the Canny Edge Detection filter was chosen as it had the highest classification accuracy of 95.7% and an F1 score of 96.1. Multi-level testing and data analysis was conducted for this application, it has been verified to be functional in the real world through field testing at local garden centers. This application is a novel and accessible tool for crop disease management and can be deployed as a free service to farmers for ecologically sustainable production, overall increasing food security.