A Deep Learning Approach for Prediction of Plant Diseases

Plant diseases represent a critical challenge to global food security and agricultural productivity. Timely and accurate disease prediction is essential for effective management. Recent advancements in deep learning techniques, particularly in computer vision, have shown remarkable potential in the context of plant disease prediction. This paper presents a comprehensive exploration of plant disease prediction using cutting-edge deep learning algorithms. Our proposed approach encompasses key stages, beginning with image acquisition, pre-processing, augmentation, and disease prediction. The plant leaf photos are used to train cutting-edge deep learning models, such as EfficientNet with Attention layer and DenseNet. These models are adept at learning the nuanced features that distinguish healthy and diseased plant leaves. To ensure the quality and consistency of our dataset, the employ pre-processing techniques, including image enhancement. Large-scale datasets are utilized to train convolutional neural networks (CNNs), allowing our models to glean insights from a diverse array of healthy and affected plant specimens. The result is a robust predictive framework that empowers farmers to identify diseases affecting their crops. Furthermore, our research introduces the application of image processing, with a particular focus on the EfficientNet and DenseNet Model. By isolating individual leaves within field images, the challenge is detecting diseases within complex visual contexts. This innovative approach holds great promise in enhancing the accuracy and reliability of disease prediction. In the relentless pursuit of agricultural sustainability and food security, the fusion of deep learning and plant pathology emerges as a beacon of hope. This work underscores the pivotal role of technology in safeguarding our agricultural heritage.