Revolutionizing Citrus Health: AI-Based Detection of Greening Disease and Nutrient Deficiencies in Sweet Orange
Thomse S. R.
Department of Plant Pathology, College of Agriculture, Badnapur affiliated to Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, (MS), India.
Ghante P. H
Agricultural Research Station (ARS), Badnapur 431202 (MS), India.
Hingole D. G
Division of Plant Pathology, College of Agriculture, Badnapur, 431202 (MS), India.
Suradkar A. L.
Department of Plant Pathology, India Agriculture Research, Institute, New Delhi, India.
Patil S. G
Agricultural Entomology, College of Agriculture, Badnapur, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, (MS), India.
Patil L. P
Sweet Orange Research Station (SORS), Badnapur, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, (MS), India.
Khaire P. B. *
Department of Plant Pathology and Microbiology, Mahatma Phule Krishi Vidyapeeth, Rahuri (MH), 413722, India.
Pawar S. Y
Division of Plant Pathology, College of Agriculture, Badnapur, 431202 (MS), India.
*Author to whom correspondence should be addressed.
Abstract
This paper explores the application of Artificial Intelligence (AI) techniques for detecting plant diseases, focusing on citrus greening disease and various foliar nutrient deficiencies in sweet orange. Agriculture faces numerous challenges from cultivation to harvesting, including significant yield losses due to disease infections and environmental hazards from excessive use of insecticides and fungicides. As the global population grows, the demand for food is surging, and traditional farming methods fall short in meeting this demand, often degrading soil health through intensive pesticide use. AI offers significant advantages over conventional techniques in disease detection. This study employs AI for visual detection of citrus greening disease and foliar nutrient deficiencies, achieving 87% accuracy on a test dataset of infected, nutrient-deficient, and healthy sweet orange leaves. Performance is evaluated using metrics such as Accuracy, Recall, Precision, and F1-Score. Specifically, Convolutional Neural Network (CNN) architectures, including Visual Geometry Group (VGG-16), are utilized for image-based detection and classification, demonstrating the potential of AI in enhancing plant health monitoring.
Keywords: Nutrient deficiencies, convolutional neural network (CNN), visual geometry group (VGG), accuracy, recall, precision and f1-score