The recognizable proof of plant sickness is the reason for the counteraction of plant infection proficiently and definitely in unpredictable climate. This causes a huge degree of demolition of harvests, reduces advancement and eventually prompts money-related loss of farmers. In view of a quick improvement in a grouping of ailments and adequate data on farmers, recognizing verification and treatment of the ailment has become a huge test. The leaves have surface and visual resemblances, which credit conspicuous verification of sickness type. Therefore, PC vision used with significant learning is the best way to deal with tackle this issue. This paper proposes a significant learning-based model, which is readied using the Plantvillage dataset containing pictures of strong and undesirable yield leaves. The model serves its objective by organizing pictures of leaves into unfortunate classes reliant on the case of flaw. Utilizing a Plantvillage dataset of 3900 pictures of ailing and solid plant leaves gathered under controlled conditions, we train a profound convolutional neural organization to distinguish 11 yield species and 26 illnesses (or nonappearance thereof). Batch Normalization is performed to forestall network over-fitting while at the same time upgrading the heartiness of the model. Prelu activation function and Adam optimizer are utilized to improve both assembly and exactness. The prepared model accomplishes a precision of 98.74% on a held-out test set, showing the achievability of this methodology.