Segmentation and classification of plant leaf disease using advanced deep learning approach and ensemble classifier

Abstract

An essential component of maintaining global food production is plants. On other hand, a number of plant diseases can threaten agricultural output and cause large losses if left unchecked. Agricultural specialists and botanists physically track plant diseases in a labor-intensive, error-prone manner using a conventional method. AI can give evaluations that are quicker and more accurate than those made using conventional approaches by automating the identification and analysis of diseases. This technical development presents a viable way to lessen crop losses and lessen the severity of infections. As a result, we describe an ensemble machine learning strategy for plant disease classification in this study that is enabled by deep learning. Data augmentation is done in the first part of the study, and in the second step, we provide a modified Mask R-CNN model for plant leaf segmentation. Afterwards, a model to extract the deep features based on CNN is shown. Lastly, the ensemble classifier is built using support vector machine classifier (SVM), random forest (RF), and decision tree (DT) with the aid of majority voting. The suggested method's effectiveness is tested on plant village, apple, maize, and rice, yielding overall accuracy values of 99.45%, 96.30%, 96.85%, and 98.25%, in that order.

Plant phenotyping relevance

植物葉の病害状態を画像からセグメンテーション・分類する深層学習手法の開発と精度評価が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper uses PlantVillage and other leaf-disease image datasets and a Mask R-CNN + ensemble classifier pipeline, but provides no public code, model checkpoints, or paper-specific data deposit. The DATA AVAILABILITY statement only says data are available within the article, and no author repository URL is given. The D

No evidence-backed public reproduction asset is currently recorded.