Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6596
Keywords:
Convolution Neural Network (CNN), supervised learning, plant leaves dataset, VGG16, riceleaves diseases

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Jyoti Dinkar Bhosale
Assistant Professor, Department of Computer Engineering, VDF Group of Institutions School of Enginerring and Technology, Latur, India-413512
Sushma Sambhaji Thorat
Assistant Professor, Department of Civil Engineering, VDF Group of Institutions,School of Engineering and Technology, Latur, India-413512
Priti Vijaykumar Pancholi
Assistant Professor, Department of Computer Engineering, MBES College of Engineering, Ambajogai, India
Prasad Raghunath Mutkule
Assistant Professor, Department of Information Technology, SRES’s Sanjivani College of Engineering, Kopargaon, Maharashtra, India- 423603

Abstract

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on.

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How to Cite
Bhosale, J. D. ., S. S. . Thorat, P. V. . Pancholi, and P. R. . Mutkule. “Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 5s, May 2023, pp. 45-50, doi:10.17762/ijritcc.v11i5s.6596.
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Vol. 11 No. 5s (2023): (Articles in Press)
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Articles

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