Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Bharathwaaj Sundararaman, Siddhant Jagdev, Narendra Khatri

Research output: Contribution to journalReview articlepeer-review


The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields in the face of numerous challenges, including plant diseases. Tomato is a widely cultivated and essential food crop that is particularly susceptible to disease, resulting in significant economic losses and hindrances to food security. Recently, Artificial Intelligence (AI) has emerged as a promising tool for detecting and classifying tomato leaf diseases with exceptional accuracy and efficiency, empowering farmers to take proactive measures to prevent crop damage and production loss. AI algorithms are capable of processing vast amounts of data objectively and without human bias, making them a potent tool for detecting even subtle variations in plant diseases that traditional techniques might miss. This paper provides a comprehensive overview of the most recent advancements in tomato leaf disease classification using Machine Learning (ML) and Deep Learning (DL) techniques, with an emphasis on how these approaches can enhance the accuracy and effectiveness of disease classification. Several ML and DL models, including convolutional neural networks (CNN), are evaluated for tomato leaf disease classification. This review paper highlights the various features and techniques used in data acquisition as well as evaluation metrics employed to assess the performance of these models. Moreover, this paper emphasizes how AI techniques can address the limitations of traditional techniques in tomato leaf disease classification, leading to improved crop yields and more efficient management techniques, ultimately contributing to global food security. This review paper concludes by outlining the limitations of recent research and proposing new research directions in the field of AI-assisted tomato leaf disease classification. These insights will be of significant value to researchers and professionals interested in utilizing ML and DL techniques for tomato leaf disease classification and ultimately contribute to sustainable food production (SDG-3).

Original languageEnglish
Article number11681
JournalSustainability (Switzerland)
Issue number15
Publication statusPublished - 08-2023

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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