Leaf Disease Detection using Deep Learning Algorithm
Kishori Patil1, Santosh Chobe2
1Kishori Patil, Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Savitribai Phule Pune University, Pune, India.
2Santosh Chobe, Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Savitribai Phule Pune University, Pune, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3172-3175 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5965029320/2020©BEIESP | DOI: 10.35940/ijeat.C5965.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease and reduce the unnecessary drug usage. Earlier, automatic detection of plant disease was performed by image processing. For disease detection and classification, image processing tools and the machine learning mechanism are proposed. Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, feature classification, disease prediction and fertilizer recommendation.detection of disease is important because it will may help farmers to provide proper solution to prevent these disease.
Keywords: Classification, Feature Extraction, Image Global Features, Image Processing, Machine Learning.