A Review of Deep Convolutional Neural Networks Applications in Detection of Tomato Diseases using Leaf Images

Tomato Plant diseases is one of the major contributors to production loss. Several methods such as manual inspection have been used in the past to detect tomato diseases but were found ineffective. The recent developments in the field of computer vision and deep learning has made the issue of automatic diseases recognition from plants using convolutional neural networks a reality. Automated detection of plant diseases have made the detection reliable, rapid and early and have solved the shortcomings of human inspection. The early detection of tomato diseases will lead to proper monitoring and higher yield. The automatic detection method is not trivial. This paper presents a survey of related works of application of convolutional networks (CNN)to detection of Tomato plant diseases. In this research, 92 journals were reviewed on the use of CNN to detect Tomato leaf diseases over the period of five years (2020- 2025). Finally, the benefits and short-comings of CNN in Tomato diseases identification were highlighted.