Securing potato Forms: A holistic approach to Classification of Early Blight and Late Blight Disease Management

Authors

  • Gopesh Kumar Bharti Department of Computer Science Engineering, Raipur Institute of Technology, Raipur, Raipur, India
  • Mr.Mahadev Bag Department of Computer Science, Raipur Institute of Technology, Raipur, Raipur, India
  • Dr. Tanushree Chatterjee Department of Biotechnology, Raipur Institute of Technology, Raipur, Raipur, India
  • Dr. Vivek Kumar Sinha Department of Computer Science, Lovely Professional University , Punjab

Keywords:

Disease detection, Preprocessing, Classification

Abstract

Potato cultivation faces significant economic challenges due to prevalent disease, particularly early blight and late blight. Early blight, caused by Alternaria solani, affects leaves and stems, potentially leading to defoliation and increased tuber infection risk. Late blight, attribute to phytophthora infestans, is a critical potato disease, capable of causing rapid crop failures. Timely disease detection is crucial for farmers to implement tailored treatments and minimizing financial losses. Early blight, associated witg fungi, and late blight, linked to specific microorganisms, require distinct control strategies, highlighting the need for accurate disease identification. This study introduces a deep convolutional neural network architecture with 14 layers, incorporating two primary convolutional layers for feature extraction with varying windows size, followed by two fully connected layers for classification. Augmentation techniques were applied to a dataset comprising 2152 images, resulting in a substantial enhancement in overall testing accuracy. The proposed architecture achieved an impressive mean testing accuracy of 98.43%. To ensure the robustness of the result, over six performance metrics were employed. This innovative deep learning approach holds promise for revolutionizing potato disease classification and advancing precision agriculture practices. The architecture implemented in a Jupyter notebook with GPU acceleration, demonstrated its effectiveness through meticulous testing and validation. The study underscores the potential of deep learning in providing accurate and efficient solutions for potato disease management, offering a transformative impact on agricultural practices and crop yield optimization.

References

Anand H. Kulkarni, Ashwin Patil R. K., Applying image processing technique to detect plant diseases, International Journal of Modern Engineering Research, vol.2, Issue.5, pp: 3661-3664, 2012.

F. Argenti,L. Alparone,G. Benelli ,” Fast algorithms for texture analysis using co-occurrence matrices” Radar and Signal Processing, IEE Proceedings , vol. 137, Issue 6, pp:443-448 , No. 6, December 1990.

P. Revathi, M. Hemalatha, Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques, IEEE International Conference on Emerging Trends in Science, Engineering and Technology, pp-169-173, Tiruchirappalli, Tamilnadu, India, 2012.

Tushar H. Jaware, Ravindra D. Badgujar and Prashant G. Patil, Crop disease detection using image segmentation, National Conference on Advances in Communication and Computing, World Journal of Science and Technology, pp:190-194, Dhule, Maharashtra, India, 2012.

Prof. Sanjay B. Dhaygude, Mr.Nitin P. Kumbhar, Agricultural plant Leaf Disease Detection Using Image Processing, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , S & S Publication vol. 2, Issue 1, pp: 599-602, 2013.

Mokhled S. Al-Tarawneh An Empirical Investigation of Olive Leave Spot Disease Using Auto-Cropping Segmentation and Fuzzy C-Means Classification, World Applied Sciences Journal, vol.23, no.9, pp:1207-1211,2013.

Yan-Cheng Zhang, Han-Ping Mao, Bo Hu, Ming -Xi Li, Feature Selection of Cotton Disease leaves Image Based on Fuzzy feature Selection Techniques, Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, pp:124-129, Beijing, China, Nov. 2007.

S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1–13, 2010.

A.-K. Mahlein, E.-C. Oerke, U. Steiner, and H.-W. Dehne,“Recent advances in sensing plant diseases for precision crop protection,” European Journal of Plant Pathology, vol. 133, no. 1, pp. 197–209, 2012.

Yuan Tian, Chunjiang Zhao, Shenglian Lu and Xinyu Guo. "SVMbased Multiple Classifier System for Recognition of Wheat Leaf Diseases" Proceedings of 2010 Conference on Dependable Computing (CDC’2010) November 20-22, 2010, Yichang, China.

11 M. S. Prasad Babu and B. Srinivasa Rao [2007] Leaves Recognition Using Back Propagation Neural NetworkAdvice For Pest and Disease Control On Crops, IndiaKisan.Net: Expert Advissory System.

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Published

2023-12-28

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Articles

How to Cite

Securing potato Forms: A holistic approach to Classification of Early Blight and Late Blight Disease Management . (2023). International Journal of Futuristic Innovation in Engineering, Science and Technology (IJFIEST), 2(3), 167-174. https://journal.inence.org/index.php/ijfiest/article/view/238

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