Intelligent Plant Disease Identification Using Deep Learning and Computer Vision

Abstract

Plant diseases are generally caused by pest, insects, pathogens and decrease the productivity to large scale if not controlled within time. Agriculturists are facing lose due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge that is in acres. The proposed system provides the solution for regularly monitoring the cultivated area and provides the automated disease detection using remote sensing images. The proposed system intimates the agriculturist about the crop diseases to take further actions. The objective of the proposed system is to early detection of diseases as soon as it starts spreading on the outer layer of the leaves. The proposed system works in two phases: the first phase deals with training data sets. This includes, training both healthy and as well as diseased data sets. The second phase deals with monitoring the crop and identifying the disease using Canny?s edge detection algorithm.

Plant phenotyping relevance

植物葉の画像から病害状態を自動検出するコンピュータビジョン手法が研究の中心であり、植物の病害表現型を直接推定しているため。

Code and data availability

The paper describes a plant disease detection system using standard datasets (PlantVillage, PlantDoc) and CNN methods, but provides no author-specific public dataset, code, model checkpoint, or supplement with availability language or URLs. PlantVillage is a generic third-party dataset, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.