Abstract

The coffee industry plays a crucial role in global agriculture and economy. Monitoring the health and classification of coffee plants is vital for optimizing yield and ensuring sustainable production. Coffee plants are very vulnerable to several diseases and pests. The long-term effects of excessive pesticide usage may enhance disease resistance, severely limiting coffee plants' ability to fend off infections. The goal of this project is to create a sophisticated system that employs a deep learning-based Sequential Convolutional Neural Network (CNN) model to visualise and categorise coffee leaves. This study provides a unique method for visualising and categorising coffee leaves using a deep learning-based Sequential CNN model. Coffee plant growers may be able to spot infections more promptly with the aforementioned approach, enhancing India's coffee crop output. The suggested method proposing an accuracy of 97% was created to aid farmers and the agriculture industry. Hence, the created model shows promising accuracy and interpretability outcomes, leading to the growth of precision agriculture in the coffee business.