Scopus Indexed Publications

Paper Details


Title
Pumpkin Leaf Disease Detection: Convenience of CNN Over Traditional Machine Learning in Terms of Image Classification
Author
Mosaddek Ali Mithu, Abdus Sattar, Kazi Motiour Rahman, MD. MEHEDI HASAN, Shampa Islam Momo,
Email
Abstract

Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern image processing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.

Keywords
Machine learning Data analysis Classification Detection Leaf disease Image processing Convolutional neural network (CNN)
Journal or Conference Name
Smart Innovation, Systems and Technologies
Publication Year
2022
Indexing
scopus