Open Access

ARTICLE

Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

by Shaheen Khatoon^1,*, Md Maruf Hasan¹, Amna Asif¹, Majed Alshmari¹, Yun-Kiam Yap²

1 College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
2 College of Science, King Faisal University, Al-Ahsa, 31982, Saudi Arabia

* Corresponding Author: Shaheen Khatoon. Email:

(This article belongs to the Special Issue: Deep Learning Trends in Intelligent Systems)

Computers, Materials & Continua 2021, 67(1), 595-612. https://doi.org/10.32604/cmc.2021.014580

Received 01 October 2020; Accepted 07 November 2020; Issue published 12 January 2021

Abstract

Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect diseases. In this work, we proposed an AI-based approach to detect diseases in tomato plants. Our goal is to develop an end-to-end system to diagnose essential crop problems in real-time, ensuring high accuracy. This paper employs various deep learning models to recognize and predict different diseases caused by pathogens, pests, and nutritional deficiencies. Various Convolutional Neural Networks (CNNs) are trained on a large dataset of leaves and fruits images of tomato plants. We compared the performance of ShallowNet (a shallow network trained from scratch) and the state-of-the-art deep learning network (models are fine-tuned via transfer learning). In our experiments, DenseNet consistently achieved high performance with an accuracy score of 95.31% on the test dataset. The results verify that deep learning models with the least number of parameters, reasonable complexity, and appropriate depth achieve the best performance. All experiments are implemented in Python, utilizing the Keras deep learning library backend with TensorFlow.

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