Disease Detection in Fruit Plants Using Machine Learning Techniques

Abstract

Fruit crops such as apple, grape, and tomato suffer significant yield loss every year due to leaf diseases that are usually identified by manual visual inspection, a process that is slow, subjective, and depends heavily on the availability of trained agronomists. This paper presents a machine-learning-based system that classifies fruit leaf images into healthy and diseased categories directly from RGB leaf photographs. Leaf images from three fruit crops (apple, grape, and tomato), covering twelve disease and healthy classes, are collected from the public PlantVillage repository. Each image is resized, normalized, and passed through a preprocessing pipeline before two parallel classification routes are evaluated: (i) classical machine learning models — Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest — trained on handcrafted color, texture (GLCM), and shape (HOG) features, and (ii) a lightweight convolutional neural network built by finetuning MobileNetV2 through transfer learning. On a held-out test set of 2,560 images, the proposed transfer-learning model achieves the highest illustrative accuracy of 96.3%, outperforming the best classical model (Random Forest, 92.1%) by 4.2 percentage points. The results indicate that a lightweight transfer-learning model offers a practical balance between accuracy and computational cost for on-field, low-resource disease screening in fruit orchards.