AI MODEL FOR IDENTIFICATION OF MICRONUTRIENT DEFICIENCY IN BANANA CROP

Abstract

This research presents an advanced convolutional neural network (CNN) model for diagnosing micro-nutrient deficiencies in banana crops through the analysis of leaf images. Proper nutrition is essential for optimal crop growth and yield, and deficiencies in vital nutrients can severely impact plant health and productivity. To address this, we have developed a specialized CNN model designed to detect and classify various nutrient deficiencies based on detailed leaf images. The study involves a comprehensive dataset of banana leaves exhibiting different deficiency symptoms, which was used to train and evaluate the model. The CNN architecture was carefully optimized to enhance feature extraction and classification capabilities, enabling precise identification of nutrient-related issues. The findings demonstrate the model’s effectiveness in distinguishing between different types of nutrient deficiencies, providing a valuable tool for precision agriculture. This approach aims to improve nutrient management practices and contribute to better crop health monitoring, highlighting the significant role of machine learning technologies in advancing agricultural research and practices.