Detecting Multiple Diseases in Multiple Crops Using Deep Learning

TL;DR

This paper presents a deep learning model trained on a comprehensive dataset to detect multiple crop diseases across 17 crops, achieving 99% accuracy and surpassing previous methods in scope and performance.

Contribution

The study introduces a unified dataset and a deep learning approach that improves detection accuracy and covers more crops and diseases than existing solutions.

Findings

Achieved 99% detection accuracy on the unified dataset.

Outperformed state-of-the-art models in accuracy and scope.

Covered 17 crops and 34 diseases, expanding detection capabilities.

Abstract

India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.