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

Suma S. Huddar, Jayashri Rudagi, Jagadish S. Jakati

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.

Keywords


Classification; CNN Mask R-CNN; Ensemble; Plant disease; SVM

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v38.i3.pp1489-1502

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES).

shopify stats IJEECS visitor statistics