Artificial intelligence for early detection and management of Tuta absoluta-induced tomato leaf diseases: A systematic review

Abstract

Food security is a critical challenge of the 21st century, increasingly exacerbated by climate change, which facilitates the spread of pests on farms. The South American tomato pinworm, Tuta absoluta (Meyrick), represents a significant global threat to tomato crops. Rising infestations have led to the extensive use of insecticides, raising concerns about pesticide resistance, human health risks, and environmental contamination. Meanwhile, artificial intelligence (AI) provides real-time, scalable, and cost-effective alternatives to traditional pest detection methods, which are labour-intensive and prone to human error. As a result, this study comprehensively assesses the potential of AI in the early detection and mitigation of Tuta absoluta-induced tomato leaf diseases. A systematic literature review was conducted across four major academic databases: ScienceDirect, Scopus, ACM, and IEEE. After a rigorous screening process, 115 studies were selected from an initial pool of 178 papers based on the relevance of their methodologies. This paper synthesises current research on AI methodologies, pest detection technologies, and their agricultural applications for the early detection, identification, and management of Tuta absoluta-induced tomato leaf diseases. Beyond tomato crops, the findings offer broader implications for managing similar pests affecting other economically significant crops. The study concludes with actionable recommendations for integrating AI-driven pest detection into precision agriculture, with the goal of enhancing food security and promoting sustainable farming practices worldwide.