A novel approach for detecting Pomegranate leaf pathologies using deep learning

Abstract

Pomegranate cultivation is essential for ensuring global food security, yet it faces significant threats from diseases. This has resulted in significant losses of yield. Integrated disease management strategies are therefore crucial for addressing this problem and include cultural practices, disease resistant varieties and timely application of fungicides. Artificial intelligence (AI), primarily deep learning, has recently made great strides that can help change the landscape on how agricultural diseases are diagnosed. In this study, therefore, we will develop a highly accurate and efficient system for early detection of diseases in pomegranate crops using deep learning algorithms. Using convolutional neural networks (CNNs) along with transfer learning coupled with data augmentation techniques such as rotation, flipping and scaling automate the identification and classification of disorders related to leaves growing on pomegranate plants. Rigorous experimentation and evaluation using performance metrics like accuracy, precision, recall, and F1 score demonstrate the efficacy of the CNN models. The underlying theory behind this is that AI technologies would be able to successfully detect all BLB diseases associated with pomegranates at an accuracy rate of 96.33 percent using Inception v3 CNN model. These results point out that AI technology changes everything about dealing with crop diseases in agriculture while also emphasizing its importance towards global food security efforts. This study not only presents a novel approach to disease detection but also provides practical solutions for farmers to mitigate crop losses and improve yield quality, promoting more sustainable and resilient farming.