Tomato plants are vulnerable to a wide range of diseases that affect their growth and crop yield, resulting in significant economic losses for farmers. This paper provides a comprehensive analysis of tomato plant life cycle and environmental conditions required for optimum crop growth. It explores tomato varieties along with their properties, detailed information on diseases across all the growth stages, including causes, symptoms, and control measures. The paper also provides an extensive evaluation of different artificial intelligence methods for tomato plant disease detection and prediction. It is observed that the detection accuracy of AI models ranges from 92% to 99%. Additionally, the paper explores advanced technologies like drones/UAV, IoT, cloud and edge computing, and blockchain for tomato cultivation management. The advantages and challenges w.r.t each technology are analyzed. The insights and solutions discussed in this paper can help farmers and researchers effectively manage tomato plant diseases and better understand supporting technologies that can enhance productivity and enable disease prediction for sustainable tomato cultivation.
Shankarappa, G. ., & Channabasavanna, P. T. . (2026). Comprehensive Analysis on Tomato: Disease Detection, Recommendations, and Advanced Technological Approaches in AI. Current Applied Science and Technology, e0268213. https://doi.org/10.55003/cast.2026.268213
https://cast.kmitl.ac.th/doi/10.55003/cast.2026.268213
