Advancements in apple disease classification: Machine learning models, IoT integration, and future prospects

Abstract

Apple orchards are of significant importance in the global agricultural sector, but they are vulnerable to a range of diseases that have the potential to cause diminished crop productivity and financial hardships. This manuscript investigates the utilization of machine learning methodologies, such as Logistic Regression, Neural Networks, and Random Forest, to classify three prevalent apple diseases: Blotch, Normal, and Rot Scab. The performance of these models is assessed using several assessment criteria, and confusion matrices are presented to aid in the prompt and precise detection of these diseases. This supports the implementation of efficient disease control strategies in apple orchards. By utilizing these ML models for the detection and treatment of diseases, not only augment agricultural productivity but also make a valuable contribution to sustainable agricultural practices by diminishing the necessity for excessive pesticide application. The experimental results indicates that Logistic Regression reflects the best performance as compared to other machine learning models taken into consideration using the different parameters. it obtained 90.6% of AUC and 65.7% of classification accuracy as compared to NN and Random Forest, which has achieved, 89.3%, 65.1%, 80.9% and 52.2.%, respectively.