Classification and Prediction of Crop Diseases: A Review

Abstract

Agriculture is the foundation of civilization which faces several problems in the 21st century. Crop diseases threaten global food security by reducing yields. Visual inspection to detect diseases can be time-consuming, subjective, and error-prone. Recent advances in various machine learning (ML) and deep learning (DL) techniques have led to ease in the identification of crop diseases. ML and DL demonstrate their versatility in image recognition, segmentation, and anomaly detection. In this paper, some of the recent works based on crop-disease detection using various ML and DL techniques are reviewed. It includes early illness detection and appropriate interventions to reduce yield loss. The review emphasizes the relevance of crop disease detection for food security and sustainable agriculture. ML and DL techniques can help farmers monitor crop health and optimize resource allocation.