Revolutionizing Agriculture: Botani Scan’s Deep Learning for Plant Disease Diagnosis

Year : 2024 | Volume :11 | Issue : 01 | Page : –
By

Avinash A. Utikar,

Shivkumar Ankamwar,

Aarzoo Chougule,

Rohan Aher,

Abhishek Giri,

  1. Professor Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
  2. Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
  3. Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
  4. Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
  5. Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India

Abstract

Crop disease detection is of key importance because of its role in food safety but infrastructural issues still hamper diagnosis in most regions worldwide. Accurate plant disease identification is essential to secure food, predicting yield decline and managing epidemic outbursts. The advent of digital cameras along with the progress of computer vision technology brings to light the mounting demands for the development of automated disease detection methods in precision agriculture, high-yield plant phenotyping, smart greenhouses integration and so on. The Residual Network (ResNet50) was trained on the image-based dataset containing 54,306 images of crop leaves (ResNet50) and modelled for disease classifier. The test set output accuracy for the ResNet50 architecture is as high as 99.30%, which shows its efficiency. This points to the possibility of the use of ResNet models that already are trained on large-scale image datasets for global crop disease diagnosis. This solution can be used as a balanced way of dealing with crop disease identification so that food security efforts at the global level could be enhanced.

Keywords: Automated Disease Detection, Residual Network (Resnet50), Disease Classification, Accuracy, Large-Scale Image Datasets.

[This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]

How to cite this article: Avinash A. Utikar, Shivkumar Ankamwar, Aarzoo Chougule, Rohan Aher, Abhishek Giri. Revolutionizing Agriculture: Botani Scan’s Deep Learning for Plant Disease Diagnosis. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(01):-.
How to cite this URL: Avinash A. Utikar, Shivkumar Ankamwar, Aarzoo Chougule, Rohan Aher, Abhishek Giri. Revolutionizing Agriculture: Botani Scan’s Deep Learning for Plant Disease Diagnosis. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(01):-. Available from: https://journals.stmjournals.com/jotssn/article=2024/view=151377

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References

[1] Mr. P V Vinod, Mr. Ramachandra Hebbar, Shima Ramesh (2018). “Plant Disease Detection Using Machine Learning.” MVJ college of Engineering, Bangalore, India.

[2] Lili Li, Shujuan Zhang, And Bin Wang (2021). “Plant Disease Detection and Classification by Deep Learning—A Review” Shanxi Agricultural University, Jinzhong 030800, China.

[3] M. I. Pavel · R. I. Rumi · F. Fairooz · S. Jahan · M. A. Hossain (2020). “Deep Residual Learning Approach for Plant Disease Recognition” Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.

[4] Andrew J., Jennifer Eunice, Daniela Elena Popescu (2022). “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications”. Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania.

[5] Emmanuel Moupojou, Appolinaire Tagne, Florent Retraint (2023). “Field Plant: A Dataset of Field Plant Images for Plant Disease Detection and Classification with Deep Learning.” Department of Computer Science, University of Yaoundé I, Yaoundé, Cameroon.

[6] “Deep Residual Learning Approach for Plant Disease Recognition” Monirul Islam Pavel, Roisul Islam Rumi, Fabiha Fairooz, Sigma Jahan,and Mohammad Amzad Hossain(2020).

[7] Lee, S.H.; Goëau, H.; Bonnet, P.; Joly, A. (2020) “New perspectives on plant disease characterization based on deep learning. Comput. Electron. Agric.”.

[8] Scientist, D.; Bengaluru, T.M.; Nadu, T. (2020) “Rice Plant Disease Identification Using Artificial Intelligence. Int. J. Electr. Eng. Technol.”.

[9] Sujatha, R.; Chatterjee, J.M.; Jhanjhi, N.; Brohi, S.N. (2021) “Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess. Microsyst.”.

[10] Barbedo, J.G.A. (2018) “Factors influencing the use of deep learning for plant disease recognition. Biosyst. Eng.”.

[11] Upadhyay, S.K.; Kumar, A. (2022) “A novel approach for rice plant diseases classification with deep convolutional neural network. Int. J. Inf. Technol.”.

[12] Panchal, A.V.; Patel, S.C.; Bagyalakshmi, K.; Kumar, P.; Khan, I.R.; Soni, M. (2021) “Image-based Plant Diseases Detection using Deep Learning. Mater. Today Proc.”.

[13] Shrivastava, V.K.; Pradhan, M.K.(2021) “ Rice plant disease classification using color features: A machine learning paradigm. J. Plant Pathol.”.

[14] Kaushik, M.; Prakash, P.; Ajay, R.; Veni, S. (2020) “Tomato Leaf Disease Detection using Convolutional Neural Network with Data Augmentation. In Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES).”


Regular Issue Subscription Original Research
Volume 11
Issue 01
Received May 10, 2024
Accepted May 20, 2024
Published June 4, 2024