Efficient Identification of Rice Leaf Disease Through the Use of Cutting-Edge Deep Learning Techniques

For billions of people around the world, rice serves as their primary food source, is very important for the worldwide availability of food. However, bacterial blight and other diseases have a significant influence on agricultural output, which highlights the need for precise and efficient diagnostic methods. This study presents a deep learning-based technique for dividing rice leaves into groups that are healthy and those that have Bacterial Blight. We employed two convolutional neural network architectures, MobileNet and VGG19, to evaluate their performance in this categorization test. VGG19 demonstrated its exceptional accuracy of 96.25%, demonstrating its capacity to deliver findings with remarkable precision. MobileNet offers a lightweight and computationally efficient substitute with an accuracy rate of 85%, making it ideal for resource-constrained scenarios. This study demonstrates the potential of incorporating deep learning models into automated disease detection systems, which could facilitate quick reactions and support sustainable farming practices.