A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Aug 5, 2026

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Agriculture depends on maintaining healthy, disease-free plants, and early disease detection is vital for implementing effective treatments. Tomatoes, a high-value food crop, are vulnerable to diseases caused by environmental factors, fungi, bacteria, and viruses, which can lead to major economic losses. The study evaluates tomato leaf health using image processing combined with machine learning. A dataset of 1,778 images (healthy and infected) from Samsun and Mersin is classified using 16 algorithms with hyperparameters tuned via grid search. Tests on Jetson Nano and TX2 show Random Forest achieves about 99% accuracy, enabling faster, more reliable classification for improved pest and treatment decisions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Plant disease impact and the need for early detection

Limitations of conventional inspection methods

Image-based machine learning approaches for crop diagnosis

[https://doi.org/10.5755/j02.eie.33866](https://doi.org/10.5755/j02.eie.33866) A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing Hasan Ulutas1, *, Veysel Aslantas2 1Department of Computer Engineering, Yozgat Bozok University, Ataturk Road [7.km](7.km), 66200 Yozgat, Turkey 2Department of Computer Engineering, Erciyes University, Meliksah District, 38200 Yozgat, Turkey *[hasan.ulutas@bozok.edu.tr](hasan.ulutas@bozok.edu.tr), [aslantas@erciyes.edu.tr](aslantas@erciyes.edu.tr) Abstract—Agriculture is crucial to economic growth and development, and maintaining high-quality, disease-free plants is crucial to its success. Early detection of plant diseases, which can be caused by environmental factors, fungi, bacteria, and viruses, is essential to implement appropriate treatments. Tomatoes, which are one of the most vital food crops, are susceptible to diseases that can result in significant economic losses in agriculture. This study introduces a method to evaluate the health of tomato leaf using image processing techniques and machine learning algorithms. A dataset of 1,778 images of healthy and infected tomato leaves was collected from tomato planting areas in the Turkish provinces of Samsun and Mersin. Sixteen advanced machine learning algorithms were used for classification, and the optimal hyper parameters for each algorithm were determined using a grid search approach. The classifiers were executed on Jetson Nano and TX2 embedded systems. The experimental results indicate that the Random Forest classifier outperformed other algorithms, achieving approximately 99 % accuracy in detecting and classifying the health status of tomato leaves. The proposed system enables faster and more accurate detection, allowing farmers to classify plants as infected or healthy, ultimately improving decision-making on treatment and pest management strategies. Index Terms—Classification algorithms; Image processing; Smart agriculture; Machine learning algorithms. I. INTRODUCTION Plants are critical to the environment and humanity. Without them, sustaining the ecology of the Earth would be impossible. They are widely used in various fields, including energy, industry, food, and medicine. Plant infections and diseases significantly affect crop quality and quantity. This situation has detrimental effects on the economies of nations where agriculture is the primary source of income [1] . Early detection, diagnosis, and management of crop infections are vital to reduce crop damage and maximise crop production, quality, and quantity. According to research in this field, there are Manuscript received 16 December, 2022; accepted 4 March, 2023. approximately 500,000 plant species worldwide. New species have been discovered as a result of research by plant experts, and the number of existing plant species is increasing day by day. However, certain plant species are threatened with extinction due to seasonal conditions and environmental pollution. Therefore, research in this field is essential to protect plants and discover new plant species [2]–[4] . Numerous diseases affect plants due to adverse environmental and seasonal conditions. Each year, these diseases result in significant productivity losses and economic impacts. Consequently, early detection of plant diseases and timely administration of appropriate actions are of crucial importance [5] . Experts in this field are responsible for identifying plant species and diseases. However, these processes are vital and challenging. To ensure the sensitivity and reliability of the identification results, visual examinations are typically conducted first, followed by laboratory examinations. However, these conventional methods require lengthy, tedious, and complex processes. For example, numerous biological tests and microscopic examinations must be performed to identify the species of thousands of plants. Extensive a

FAQs

What problem does the study address for tomato cultivation?

It targets early detection of tomato leaf diseases so farmers can classify plants as healthy or infected and choose timely treatment and pest management actions.

How is the dataset for classification constructed in the study?

A dataset of 1,778 tomato leaf images is collected from planting areas in Samsun and Mersin, covering both healthy and infected leaves.

Which machine learning model performs best and how accurate is it?

Random Forest outperforms other algorithms, reaching approximately 99% accuracy for detecting and classifying tomato leaf health status.

Aug 5, 2026

Upload by Himbo