Disease Detection in Black Sesame Leaf Using Digital Image Processing
Nida Gul, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Arbab Waseem Abbas, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Asfandyar Khan, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Lala Rukh, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Corresponding Author:
Nida Gul (nidagul05@gmail.com)
Abstract:
Pakistan is a predominantly agricultural country, with the agricultural sector contributing more to GDP than any other industrial sector (approximately 14% of GDP). Agriculture is one of the pillars of the economy, and the primary source of income is the farming industry. However, this industry's productivity and yield are declining due to various problems, including pests, plant diseases, and climate. It is crucial to detect plant diseases early to prevent reductions in production and yield. However, manually tracking plant diseases is impractical due to long processing times, extensive labor, and knowledge of plant diseases required. Therefore, image processing technology is used to identify plant diseases by taking a picture as input, processing it, and comparing it to a dataset of several plant leaves. The aim of this project is to create a stand-alone application that provides farmers with relevant knowledge about the disease to support monoculture farmers effectively. This study presents a system that analyzes and recognizes plant diseases using multiple image processing techniques. The results of the implementation indicate that the proposed approach may result in positive outcomes by identifying and classifying plant ailments. This essay focuses on three common plant diseases: Bacterial Blight, Cercospora Leaf Spot, and Alternaria Alternata.
Keywords:
Standalone Application; Plant Disease Detection; K-Means; GLCM; SVM; Image Processing