AI System for Assessing the Intensity of Orange Rust in Citrus Farms using Autoencoder-Driven Decision Tree

Orange rust disease is a big danger to citrus trees around the world. So, it's important to know how much damage this sickness does. This study introduces a new way of making predictions that combines two types, autoencoders and decision trees. The author used this method to accurately classify the effects caused by orange rust disease. A collection of 9000 high-quality photos showing different types of diseases on many citrus trees and various outdoor conditions was carefully gathered. Pre-processing methods made the data set standard, reducing biases and making underdog classes stronger. This new design idea has a part that takes important details and shrinks the size of images. Then, it uses a decision tree classifier to sort these features into different levels based on how much they affect disease impact. The model Autoencoder and Decision Trees had a very good 98% total accuracy. It was quite successful at guessing and sorting the seriousness of diseases properly. This study is an important tool for checking the sickness levels in orange farming. It helps people make decisions quickly and use resources well to protect crops better.