Survey of Methods to Detect Tobacco Leaf Disease

Survey of Methods to Detect Tobacco Leaf Disease

Khushi H P1, Praveen H M2, Pragna C P3, Giridhar K4, B Uma5

1,2,3,4,5Department of Computer Science and Engineering, Malnad College of Engineering, Hassan

Abstract

Tobacco is a key commercial crop in karnataka and several parts of india, but its cultivation is frequently hindered by various foliar diseases that reduce yield and quality. Traditional disease detection methods are manual, time-consuming, and require expert knowledge. Recent advances in machine learning (ml) have demonstrated significant potential in automating plant disease classification. This paper reviews ml-based approaches for tobacco leaf disease detection, covering existing datasets, models, performance factors, practical challenges, and future research opportunities.

Keywords— Tobacco Leaf Disease, Plant Disease Detection, Image Classification, Feature Extraction, Classification Algorithms, Infrared Spectroscopy, k-Nearest Neighbors