Implementation of Plant Leaf Disease Detection using K-means Clustering and Neural Networks

Abstract

Plants exist all over the place; we live, as well as places without us. Plant disease is one of the essential causes that reduces quantity and degrades quality of the agricultural merchandises. Plant diseases have turned into a terrible as it can cause significant reduction in both quality and quantity of agricultural products. Images form important data and information in biological sciences. Until recently photography was the only method to reproduce and report such data. It is difficult to quantify or treat the photographic data mathematically. This project, classifies the plant leaves and stems at hand into infected and non-infected classes. The developing software provides a fast and accurate method in which the leaf diseases are detected and classified using k-means based segmentation and neural networks-based classification. Most common diseases seen in the leaves of Tapioca and Mango are discussed here for this approach. In this paper, respectively, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of diseases that effect on plant leaves. Recognizing the disease is mainly the purpose of the proposed approach. Thus, the proposed Algorithm was tested on five diseases which influence on the plants; they are: Early scorch, Cottony mold, ashen mold, late scorch, tiny whiteness. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of leaf diseases in a little computational effort. This project gives 95% of efficiency using MATLAB simulation results.

Plant phenotyping relevance

植物葉の感染状態を画像から抽出・分類するソフトウェアと手法の開発が中心であり、植物病害状態の画像ベース表現型計測に該当する。

Code and data availability

The paper describes MATLAB-based leaf disease detection with K-means and neural networks, but provides no public dataset, image collection, code, or model deposit. The only allowed URL appears solely as a cited reference on human action recognition, unrelated to this paper's phenotyping assets.

No evidence-backed public reproduction asset is currently recorded.