Assessment of Plant Disease Identification using GLCM and KNN Algorithms
Ch Ramesh Babu1, Dammavalam Srinivasa Rao2, V. Sravan Kiran3, N. Rajasekhar4
1Ch. Ramesh Babu, Department of CSE, GCET, Hyderabad, India D. Srinivasa Rao, Dept. of Information Technology, VNRVJIET, Hyderabad, India.
2V.Sravan Kiran, Department of IT, SMEC, Hyderabad, India.
3N. Rajasekhar, Dept. of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4900-4904 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5018018520/2020©BEIESP | DOI: 10.35940/ijrte.E5018.018520

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Abstract: One of the significant segments of Indian Economy is Cultivation. Occupation to almost 50% of the nation’s labor force is delivered by Indian cultivation segment. India is recognized to be the world’s biggest manufacturer of pulses, rice, wheat, spices and spice harvests. Agronomist’s financial progress is contingent on the excellence of the goods that they yield, which depend on on the plant’s progress and the harvest they get. Consequently, in ground of cultivation, recognition of disease in plants shows an involved part. Plants are exceedingly disposed to to infections that disturb the progress of the plant which in chance distresses the natural balance of the agronomist. In order to distinguish a plant disease at right preliminary period, usage of automatic disease detection procedure is beneficial. The indications of plant diseases are noticeable in various portions of a plant such as leaves, etc. Physical recognition of plant disease by means of leaf descriptions is a wearisome job. The k-mean clustering procedure is utilized for the segmentation of input images. The GLCM (gray-level co-occurrence matrices) procedure is utilized which excerpts textural features from the input image and implementation of KNN (k-nearest neighbors) algorithm for image classification and produced classification accuracy from 70 to 75% for different inputs. Hence, it is required to develop machine learning based computational methods which will make the process of disease detection and classification using leaf images automatic. .. To advance concert of standing methods machine learning and deep learning algorithms will be utilized for more accurate classification.
Keywords: GLCM, K-Means, KNN Algorithm, Bacterial, Fungal, Viral, Machine Learning, Deep Learning, Neural Networks, Support Vector Machines, Genetic Algorithm, Convolution Neural Networks.
Scope of the Article: Machine Learning.