Paper Key : IRJ************213
Author: Tejas Chaudhari ,Anushka Raut,Devraj Jadhavrao
Date Published: 06 Nov 2023
Abstract
The agricultural business is critical to maintaining global food security, and the health of fruit crops is critical to providing a consistent food supply. Fruit infections are a major danger to crop productivity and quality, making early detection and management critical. The merging of technology and artificial intelligence has improved fruit disease diagnosis in recent years, providing more accurate and efficient solutions. This abstract provides an overview of the approaches and challenges involved in detecting fruit disease. This review focuses on several fruit disease detection strategies, such as computer vision, machine learning, and sensor-based systems. Deep learning techniques in computer vision have enabled the automatic diagnosis of illness signs based on image analysis. Machine learning models, such as neural networks and support vector machines, have been deployed to classify disease types, predict disease severity, and assist in decision-making for disease management. Sensor-based techniques, like hyperspectral imaging and electronic nose systems, offer non-invasive and real-time monitoring of fruit health. Despite the progress in fruit disease detection techniques, several challenges persist. These include data acquisition and labelling, the need for robust and transferable models, scalability, and the integration of multiple technologies. Furthermore, the deployment of these technologies in the field may require addressing issues related to resource constraints, infrastructure, and the digital divide in agricultural communities
DOI LINK : 10.56726/IRJMETS45799 https://www.doi.org/10.56726/IRJMETS45799