<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages can drastically help in fending off the reduction in productivity/profit. Providing an intelligent and automated solution becomes a necessity. This can be achieved with the help of machine learning techniques. It involves a number of steps like image acquisition, image pre-processing using filtering and contrast enhancement techniques. Image segmentation, which is a crucial part in disease detection system, is done by applying genetic algorithm and the colour, texture features extracted using a local binary pattern. The novelty of this approach is applying the genetic algorithm for image segmentation and combining a set of propositions from all the learning classifiers with an ensemble method and calculating the results. This obeys the optimistic features of all the learning classifiers. System accuracy is evaluated using precision, recall, and accuracy measures. After analysing the results, it clearly shows that the ensemble models deliver very good accuracy of over 92 % as compared to an individual SVM, Naïve Bayes, and KNN classifiers.</p>
</abstract>

Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages can drastically help in fending off the reduction in productivity/profit. Providing an intelligent and automated solution becomes a necessity. This can be achieved with the help of machine learning techniques. It involves a number of steps like image acquisition, image pre-processing using filtering and contrast enhancement techniques. Image segmentation, which is a crucial part in disease detection system, is done by applying genetic algorithm and the colour, texture features extracted using a local binary pattern. The novelty of this approach is applying the genetic algorithm for image segmentation and combining a set of propositions from all the learning classifiers with an ensemble method and calculating the results. This obeys the optimistic features of all the learning classifiers. System accuracy is evaluated using precision, recall, and accuracy measures. After analysing the results, it clearly shows that the ensemble models deliver very good accuracy of over 92 % as compared to an individual SVM, Naïve Bayes, and KNN classifiers.

Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach

Kavayitri Bahinabai Chaudhari North Maharashtra University

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently,...

Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach

Publié en ligne: 17 août 2023

Pages: 92 - 99

DOI: https://doi.org/10.2478/acss-2023-0009

Mots clés
Banana, diseases, ensemble learning, local binary pattern

© 2023 Vandana Chaudhari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach

Publié en ligne: 17 août 2023

Pages: 92 - 99

DOI: https://doi.org/10.2478/acss-2023-0009

Mots clésBanana, diseases, ensemble learning, local binary pattern

© 2023 Vandana Chaudhari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Banana, diseases, ensemble learning, local binary pattern