Plant Disease Recognition (Integrating Deep Learning for Accurate Diagnosis and Sustainable Agriculture)

The critical role of early and accurate plant disease detection in agriculture underscores the need for advanced and reliable methods. This research explores the application of deep learning, specifically the ResNet-50V2 and DenseNet121 convolutional neural network architecture, in identifying plant diseases from image data. Utilizing a substantial dataset comprising various common plant diseases, the study implements ResNet-50V2 and DenseNet121 for image-based classification tasks. The model, pre-trained on ImageNet and fine-tuned on our specific dataset, demonstrates significant potential in accurately recognizing diverse plant diseases. The research further delves into the model's training process, optimization strategies, and the balance between complexity and performance. Key findings highlight the model's high accuracy and efficiency in disease detection, marking a substantial advancement over traditional methods. The implications of this study not only contribute to the technological enhancement of agricultural practices but also pave the way for future research in applying deep learning models in practical, real-world scenarios. The paper aims to provide insights into the integration of AI in agriculture, offering prospects for increased crop yield and health through early disease detection.