Advanced Deep Learning and Nonlinear Mathematical Analysis for Precision Detection and Targeted Treatment of Plant Diseases

Abstract

Plant diseases are a big problem in agriculture because they lower yields around the world. Regular ways of finding and treating diseases usually require a lot of work, take a long time, and aren't always accurate. For these problems, this study shows a combined method using advanced deep learning methods like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and MobileNet, along with nonlinear mathematical analysis to improve the accuracy of finding plant diseases and making the best targeted treatment plans. Image-based diagnosis of plant diseases using deep learning methods, especially CNNs, is a quick and accurate method. They learn to spot tiny visual clues that point to different diseases by looking at large collections of images of healthy and sick plants. Addition of LSTMs and MobileNet with optimization improves the model's ability to handle time cycles and complex patterns, making it more reliable and usable for a wider range of plant types and weather conditions. The deep learning models are also fine-tuned using an optimization method, like the Adam optimizer, which ensures the best performance and faster resolution. Additionally, complex mathematical models are created to show how plant diseases spread and get worse in crop fields. Based on things like disease transfer rates, weather conditions, and plant chemistry, these models can predict how diseases will spread and help with treatment plans. This system gives dynamic, site-specific advice on how to control diseases by combining these models with real-time data from field devices and remote sensing technologies. Testing the suggested method's effectiveness in the field on many different crops shows that it works much better than traditional methods at both finding problems and treating them.