International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 1 | Views: 68 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Experimental Result Paper | Computer Science | India | Volume 12 Issue 10, October 2023

Enhancing Apple Leaf Disease Identification through Optimized Machine Learning Algorithms

R Ravikumar | V Arulmozhi

Abstract: Apple orchards play a vital role in global food production, but they face constant threats from diseases like scab, rust, and rot. Accurate and timely identification of these diseases is paramount to prevent crop losses and ensure food security. In this study, we present a novel approach that integrates the Firefly Optimization (FFO) algorithm with Rough Set Theory (RST) to optimize feature selection for the identification of apple leaf diseases. We employ the K - Nearest Neighbors (k - NN) machine learning algorithm to classify various types of apple leaves, including healthy, rot, rust, and scab leaves. The results reveal the remarkable effectiveness of the optimized k - NN algorithm in comparison to the non - optimized version. The optimized approach significantly improves the True Positive Rate (TPR) and reduces the False Negative Rate (FNR) for each type of apple leaf disease, indicating enhanced accuracy in disease identification. These findings have substantial practical implications for apple growers, enabling informed management decisions to reduce crop losses. Furthermore, this study underscores the potential of machine learning, image processing, and feature selection techniques in advancing disease identification accuracy, with broader applications in agriculture. Ultimately, this research contributes to sustainable agricultural practices and global food security by harnessing technology to combat crop diseases effectively.

Keywords: Firefly Optimization, Roughset Theory, Apple orchards, Disease severity measurement, Image processing techniques, Orchard management, Precision agriculture

Edition: Volume 12 Issue 10, October 2023,

Pages: 357 - 360

How to Download this Article?

Type Your Valid Email Address below to Receive the Article PDF Link

Verification Code will appear in 2 Seconds ... Wait