Analysis on Early Prediction of Cotton Plant Leaf Diseases Using CatBoost Algorithm

As the global population steadily increases, the imperative for improved crop production becomes increasingly critical to ensure food security for everyone. Addressing agricultural diseases is a primary step, requiring the identification and widespread monitoring of these ailments. To solve this problem, the proposed project introduces a robust Cotton Leaf Diseases Detection System employing advanced machine learning techniques. Leveraging the VGG16 model, the system extracts essential features from cotton leaf images and stores them in a CSV file. The Cat Boost algorithm is then applied to the datasets for training, resulting in the creation of a highly accurate model file. This model showcases excellent classification capabilities, allowing for the detection of various cotton leaves diseases. The project optimizes the accuracy and efficiency of disease detection by integrating deep learning and gradient boosting. The integration of image feature extraction and Cat Boost algorithm not only enhances the system's performance but also offers a scalable and adaptable solution for real-world applications. This pioneering approach represents a notable progression in precision agriculture, contributing significantly to the early and accurate diagnosis of cotton leaf diseases. This, in turn, contributes to more effective crop management strategies.

I. Introduction

Agriculture assumes a crucial role in supporting global populations, and cotton serves as a vital cash crop with substantial contributions to the textile industry. To counter the diverse diseases that pose threats to cotton crops, putting both production and quality at risk, there is a need for timely and accurate detection. This sets the stage for implementing effective management strategies and securing agricultural sustainability. This project aims to develop an advanced Cotton Leaf Diseases Detection System by integrating image feature extraction using the VGG16 model with the powerful Cat Boost algorithm for classification. This project aims to develop an advanced Cotton Leaf Diseases Detection System by integrating image feature extraction using the VGG16 model with the powerful Cat Boost algorithm for classification. Being a machine learning algorithm, the robust gradient boosting algorithm is selected for its efficient handling of categorical features. Its ability to utilize extracted features for classification, combined with Python implementation and data compatibility, makes it a strong solution for cotton leaf disease identification. With the ongoing expansion of the global population, there is a growing necessity to elevate overall crop production to secure food safety for the entire populace. Addressing crop diseases becomes pivotal, necessitating their identification and widespread monitoring. To tackle this challenge, the proposed project introduces a resilient Cotton Leaf Diseases Detection System, leveraging advanced machine learning techniques. Leveraging the VGG16 model, the system extracts essential features from cotton leaf images and stores them in a CSV file. The datasets undergo training using the Cat Boost algorithm, leading to the development of a highly accurate model file. This model showcases outstanding classification capabilities, enabling the recognition of various cotton leaf diseases. The project improves detection accurate and effectiveness through deep learning and using gradient boosting. The integration of image feature extraction and Cat Boost algorithm not only enhances the system's performance but also offers a scalable and adaptable solution for real world applications. This inventive approach represents a notable advancement in precision agriculture, playing a pivotal role in achieving early and precise diagnoses of cotton leaf diseases. The ultimate goal is to enhance overall crop management for better agricultural outcomes.