Performance Analysis of Tea Leaf Disease Detection by Utilizing Hybrid ResNet and BiLSTM

This research study aims to build the ability to scan and differentiate whether a tea leaf is diseased or not. With the software built, leaf disease can be easily diagnosed and treated, which is the biggest dilemma of agriculture as sometimes its determination whether a plant is diseased or healthy gets quite hard. According to the proposed model, the disease diagnosis shall be done by an image processing and machine learning algorithm, which will offer its result through the creation of software. In this research, the optimized code is used to reduce the time complexity and space complexity of the output, and results produced are more accurate when compared to many machine learning techniques. This research study has proposed a coherent image processing system driven by machine learning techniques that is able to detect sick leaves from given tea leaf images.