Plants are prone to different diseases caused by
multiple reasons like environmental conditions, light, bacteria,
and fungus. These diseases always have some physical
characteristics on the leaves, stems, and fruit, such as changes in
natural appearance, spot, size, etc. Due to similar patterns,
distinguishing and identifying category of plant disease is the
most challenging task. Therefore, efficient and flawless
mechanisms should be discovered earlier so that accurate
identification and prevention can be performed to avoid several
losses of the entire plant. Therefore, an automated identification
system can be a key factor in preventing loss in the cultivation and
maintaining high quality of agriculture products. This paper
introduces modeling of rose plant leaf disease classification
technique using feature extraction process and supervised
learning mechanism. The outcome of the proposed study justifies
the scope of the proposed system in terms of accuracy towards the
classification of different kind of rose plant disease.
Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images
