Year: 2025
Type: article
Abstract: This project presents a smart and simple system to detect potato leaf diseases using Artificial This paper presents an AI-powered system to detect diseases in potato plants using image classification techniques. The model is built using Python and a Recurrent Neural Network (RNN). Potato crops are commonly affected by diseases like early blight and late blight, which reduce crop quality and yield. Manual detection is time-consuming and often inaccurate. Our automated system allows users to take a picture of a potato leaf using a smartphone or camera. The image is then processed by the trained RNN model, which accurately predicts the disease category. The system is affordable, easy to use, and helpful for small and large-scale farmers. Experimental results show over 94% accuracy with fast prediction time. This can help in real-time disease monitoring and smart farming applications.
Authors: Kaliselvam R, Prem, Kumaran, G. Thiagarajan, A.S. Ramakrishnan
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409958549