• Dr. Pankaj Malik
• Kailash Kumar Baraskar
• Ankita Chourasia

Plant diseases pose a significant threat to agricultural productivity and food security. Early and accurate detection of these diseases is crucial for effective disease management. In recent years, machine learning techniques have shown great potential in plant disease detection. This research paper presents a novel approach that combines the use of Gray Level Co-occurrence Matrix (GLCM) features and K-Nearest Neighbors (KNN) classification within a neural network framework. The proposed methodology involves collecting and preprocessing a dataset of plant images. GLCM features are extracted from the preprocessed images, capturing important texture information. The KNN algorithm is then utilized for disease classification based on these features. To enhance the performance and accuracy, the KNN classification is integrated into a neural network architecture.

Plant dieses, Plant dieses using MI

"Plant Disease Detection Using GLCM and KNN Classification in Neural Networks Merged with the Concepts of Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.b680-b685, July-2023, Available :http://www.jetir.org/papers/JETIR2307186.pdf

"Plant Disease Detection Using GLCM and KNN Classification in Neural Networks Merged with the Concepts of Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppb680-b685, July-2023, Available at : http://www.jetir.org/papers/JETIR2307186.pdf