Revolutionizing Agricultural Health: Deep Learning for Crop Disease Diagnosis

Significant threats to global food security are posed by crop diseases, necessitating the adoption of innovative approaches for their early detection and management. Deep learning, recognized as a subset of artificial intelligence, has emerged as a promising tool for revolutionizing crop disease diagnosis through the analysis of agricultural images. This work assesses if deep learning can transform the area of agricultural health particularly regarding crop infection analysis. Using a dataset from the Plant Village, this paper develops a deep learning model and through review of the literature, assesses the effectiveness of the deep learning model in crop disease identification and classification into categories found in leaf descriptions. The method included several explanation steps comprising data apparition, model building, training, with evaluation. Outcomes have been very effective in displaying the efficacy of deep learning models to accurately diagnose crop infections.