Crop Leaf Disease Detection and Classification Using Deep Learning

Abstract

Agriculture is often referred to as the backbone of the Indian economy, and plays a vital role in employment, food security, and national income. Farmers suffer heavy losses every year due to late detection of crop diseases. Automation has helped prevent such damage to the crops to some extent however employment of artificial intelligence (AI) based solutions have proved their effectiveness in early detection of crop diseases to a large scale while making it possible for farmers to take timely measures for crop protection, thereof. In the current scenario, AI domain has been revolutionized by deep learning, a subclass of artificial neural networks (ANN) with multiple layers, achieving the state-of-the-art results in various domains. Thus, in this research article, a deep learning based crop disease detection and classification (CROP-DC) model is presented. For this, five types of crops, including apple, bell pepper, corn, potato, and tomato, have been considered. Results demonstrated the ability of proposed model using deep learning over the existing systems that relied solely on ANN alone.