• Shiv Narain Gupta
• Sourabh Bailwal
• Rohit Rai

Leaf disease pose a significant threat to food security; however their quick identification remains a problem in different region across the world due to the absence of necessary foundations. Advancements in the field of image-based techniques of plant leaf classification showed great results. In this research paper, we will use Random Forest technique to identify whether the leaf is healthy or it is infected by any disease based on results of datasets which is created. This research paper consists of several stages for the implementation such as creating datasets, Extraction of different features of leaf, classifier then training and the last step is classification. The datasets of the leaves which is infected by any disease and the healthy leaves are combined and is trained under Random Forest for the classification of the infected and the healthy leaves. A histogram of oriented gradient is used as a tool for extracting features from an image. Overall, training large data sets with machine learning enables accurate disease detection of plant leaf on large scale.

Random Forest Technique, Extraction of Features, Training of Model, Classification

"Plant Disease Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h320-h324, May-2022, Available :http://www.jetir.org/papers/JETIR2205842.pdf

"Plant Disease Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph320-h324, May-2022, Available at : http://www.jetir.org/papers/JETIR2205842.pdf