Deep Learning Models for Early Detection of Diseases in Vegetable Crops

Vegetable crops are extremely susceptible to a wide range of diseases brought on by bacteria, viruses, fungus, and environmental stresses. Reducing crop losses, guaranteeing food security, and advancing sustainable agriculture all depend on early disease identification. Conventional disease diagnosis techniques mostly rely on expert manual inspection, which is labour-intensive, time-consuming, and frequently imprecise in field settings. Plant disease identification may now be done effectively and automatically through recent developments in computer vision and deep learning technology. Common datasets, performance indicators, benefits, drawbacks, and prospects for further research are highlighted in the article. Results show that deep learning-based systems can enhance precision agriculture techniques for sustainable vegetable production and greatly increase the accuracy of disease identification.