Fruit Scan - Disease Identification in Fruits Using Image Processing

• Published in: International journal for research in applied science and engineering technology 2024-03, Vol.12 (12), p.3061-3065
• Main Author: Chaudhari, Tejas
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2024.59572

The agricultural industry plays a crucial role in sustaining global food security, and the health of fruit crops is paramount in ensuring a steady food supply. Fruit diseases pose a significant threat to crop yield and quality, making their early detection and management essential. In recent years, the integration of technology and artificial intelligence has transformed fruit disease detection, offering more accurate and efficient solutions. This abstract provides an overview of the techniques and challenges associated with fruit disease detection. This review highlights various methodologies employed in fruit disease detection, including computer vision, machine learning, and sensor-based approaches. Computer vision, powered by deep learning algorithms, has enabled the automated identification of disease symptoms 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