Deep Learning for Automated Diagnosis of Plant Diseases: A Technological Approach

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Ashok Kumar, Pankaj Kumar, Kalpna Suman


- With the world's population growing, it is vital that food crops and medicinal plants are required to be developed in large quantity. A model that can help us understand the process more comprehensively will be explored. Based on a thorough understanding, we are exploring a model that will enable us to extract the precise information and in-depth knowledge we require. There are many plants that contract lethal diseases every year, including food plants. This reduces their output rates. We must employ automated methods to locate the issue inside the facility if we do not want manufacturing prices to rise. The detection of plant diseases can be automated through robotics and image processing. As a result of recent technological advancements, we now have the ability to simplify our artwork. A deep learning and image processing approach can increase the efficiency of detection processes. A great deal of progress has been made in diagnosing plant diseases. An artificial intelligence model was trained to assist in diagnosing flora disorders. Based on the photo information (leaf), the model can be controlled. Identifying the problem and discovering the condition in this study, we propose the use of computer vision technology combined with fuzzy logic to detect and grade leaf diseases. GLCM is performed to extract texture features, and fuzzy logic is applied to grade the disease. K-means clustering is applied for determining defected areas; GLCM is used to determine defected areas; and fuzzy logic is used for diagnosing diseases. About 70% of classifications are accurate according to the model. By using Speed Up Robust Features (SURF), DENSE, and Bag of Visual Words (BOVW) in addition to the global features, the accuracy of the system can be enhanced. A treatment program will help people better understand the illness if one is offered.

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Author Biography

Ashok Kumar, Pankaj Kumar, Kalpna Suman

1Ashok Kumar

2Pankaj Kumar

3Kalpna Suman

1Associate Professor, GL Bajaj Group of Institutions, Mathura (U.P), India

2 Assistant Professor, GL Bajaj Group of Institutions, Mathura (U.P), India

3Assistant Professor, GL Bajaj Group of Institutions, Mathura (U.P), India


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Ghaiwat Savita N, Arora Parul. Detection and classification of plant leaf diseases using image processing techniques: a review. Int J Recent Adv Eng Technol 2014;2(3):2347– 812. ISSN (Online).

Dhaygude Sanjay B, Kumbhar Nitin P. Agricultural plant leaf disease detection using image processing. Int J Adv Res Electr Electron Instrum Eng 2013;2(1).

Mrunalini R Badnakhe, Deshmukh Prashant R. An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases. Int Conf Adv Inf Technol 2011;20. 2011 IPCSIT.

Arivazhagan S, Newlin Shebiah R, Ananthi S, Vishnu Varthini S. Detection of the unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR 2013;15(1):211–7.

Kulkarni Anand H, Ashwin Patil RK. Applying image processing techniques to detect plant diseases. Int J Mod Eng Res 2012;2(5):3661–4.

Bashir Sabah, Sharma Navdeep. Remote area plant disease detection using image processing. IOSR J Electron Commun Eng 2012;2(6):31–4. ISSN: 2278-2834.

Naikwadi Smita, Amoda Niket. Advances in image processing for detection of plant diseases. Int J Appl Innov Eng Manage 2013;2(11).

Patil Sanjay B et al. Leaf disease severity measurement using image processing. Int J Eng Technol 2011;3(5):297–301.

Chaudhary Piyush et al. Color transform-based approach for disease spot detection on a plant leaf. Int Comput Sci Telecommun 2012;3(6).