Machine Learning-based Disease Classification in Tomato (Solanum lycopersicum) Plants - study and image-based detection

Aug 3, 2026

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Tomato cultivation in Bangladesh faces severe losses due to susceptibility to microorganisms, parasites, and bacterial infections. Disease symptoms often appear first on roots and leaves, making early identification difficult and delaying timely protection measures. This study develops an image-processing and machine-learning workflow for rapid, reliable classification of tomato leaf images as healthy or diseased. A dataset of 250 leaf images was collected under varying illumination, angles, and distances and expanded to 529 using augmentation, then segmented with LAB conversion and OTSU to estimate affected area. Textural features were extracted and used to train SVM, KNN, and decision trees, with Quadratic SVM achieving 97.7% test accuracy, supporting nondestructive, efficient detection to reduce crop losses locally and globally.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Problem background and significance

Objectives and approach

Materials and Methods

Dataset collection and augmentation

Image preprocessing, segmentation, and feature extraction

Model training and evaluation

Results and Discussion

Classification performance and accuracy

Implications for nondestructive monitoring

Machine Learning-based Disease Classification in Tomato (Solanum lycopersicum) Plants Klasifikasi Penyakit Berbasis Pembelajaran Mesin pada Tanaman Tomat (Solanum lycopersicum) Md Towfiqur Rahman 1,2, Sudipto Dhar Dipto2, Israt Jahan June2, Abdul Momin3, Muhammad Rashed Al Mamun2,4 1 Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA 68588 2 Department of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100 Bangladesh 3 Agricultural Engineering Technology, School of Agriculture, Tennessee Tech University, Cookeville, TN 38505, USA 4 Faculty of Agriculture, Kyushu University, Fukuoka 819-0395, Japan email: [rashed.fpm@sau.ac.bd](rashed.fpm@sau.ac.bd) ARTICLE HISTORY Submitted September 6th, 2024 Accepted November 13th, 2024 Published December 31st, 2024 KEYWORDS Detection; image processing; machine learning; plant diseases; tomato KATA KUNCI Deteksi; pembelajaran mesin; pemrosesan gambar; penyakittanaman; tomat ABSTRACT In Bangladesh, tomato cultivation faces significant challenges due to its susceptibility to various microorganisms, parasites, and bacterial infections. Typically, the early symptoms of these diseases first appear in roots and leaves, complicating timely detection. This study addresses the challenge of timely and accurate detection of diseases in tomato plants, crucial for effective plant protection management. Conventional manual inspection methods are timeconsuming and subjective, resulting in delays in implementing necessary protection measures. Therefore, an image processing technique and machine learning algorithms were used for rapid and robust detection of diseases in tomato plant leaves, aiming to streamline the detection process for chemical application responses. A dataset containing 250 images of tomato plant leaves were captured under varying light intensities, eye-level angles, and distances. Image augmentation techniques were applied to increase the dataset, resulting in a total of 529 images. These images were converted to LAB color images and then OTSU algorithm was used to segment leaf images and estimate the percentage of affected diseased areas. Various textural features were also extracted from segmented leaf images to create a training dataset. Machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and decision trees, were trained and evaluated using this dataset to classify images as healthy or diseased. The Quadratic SVM algorithm provided the highest test accuracy of 97.7% for the dataset. This nondestructive processing holds immense promise for improving disease detection efficiency and reducing losses in tomato production, both locally in Bangladesh and globally. ABSTRAK Di Bangladesh, budidaya tomat menghadapi tantangan yang signifikan karenakerentanannya terhadap berbagai mikroorganisme, parasit, dan infeksi bakteri. Biasanya, gejala awal penyakit-penyakit ini pertama kali muncul di akar dan daun, sehingga menyulitkan deteksi tepat waktu. Penelitian ini membahas tantangandeteksi penyakit yang tepat waktu dan akurat pada tanaman tomat, yang sangat penting untuk manajemen perlindungan tanaman yang efektif. Metode inspeksi manual konvensional memakan waktu dan subjektif, sehingga mengakibatkan penundaan dalam menerapkan tindakan perlindungan yang diperlukan. Olehkarena itu, teknik pemrosesan gambar dan algoritma pembelajaran mesindigunakan untuk mendeteksi penyakit pada daun tanaman tomat dengan cepat dan kuat, yang bertujuan untuk merampingkan proses deteksi untuk responsaplikasi kimia. Sebuah dataset yang berisi 250 gambar daun tanaman tomatdiambil di bawah berbagai intensitas cahaya, sudut pandang, dan jarak. Teknik augmentasi gambar diterapkan untuk meningkatkan dataset, menghasilkan total 529 gambar. Gambar-gambar ini diubah menjadi gambar berwarna LAB dankemudian algoritma OTSU digunakan untuk mensegmentasi gambar daun dan memperkirakan persent

FAQs

Why is early detection of tomato diseases important in Bangladesh?

Early disease symptoms appear on roots and leaves, which complicates timely detection. Delays in identifying diseases postpone necessary protection actions, leading to greater crop losses.

How was the dataset for this study created and expanded?

A total of 250 tomato leaf images were captured under varying light intensities, eye-level angles, and distances. Image augmentation was applied to increase the dataset to 529 images.

Which method produced the best classification accuracy and what was the value?

Quadratic SVM achieved the highest test accuracy of 97.7% for classifying leaf images as healthy or diseased.

Aug 3, 2026

Upload by Levi