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Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing

Year 2024, Volume: 30 Issue: 3, 464 - 476, 23.07.2024
https://doi.org/10.15832/ankutbd.1288298

Abstract

Corn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the crop. This process typically requires highly skilled personnel, and it can be time-consuming to complete the necessary tasks. Machine learning methods can be implemented to rapidly and accurately detect disease and reduce the risk of crop failure due to disease outbreaks. This study aimed to use traditional machine learning techniques to detect maize diseases using image processing techniques. A total of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-means clustering, and a total of 4 GLCM texture features and two statistical features were extracted from the images. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (common rust and gray leaf spot) and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7%. The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively.

References

  • Chokey T & Jain S (2019). Quality Assessment of Crops using Machine Learning Techniques. 2019 Amity International Conference on Artificial Intelligence (AICAI) pp. 259–263. https://doi.org/10.1109/AICAI.2019.8701294
  • Dhingra G, Kumar V & Joshi H D (2019). A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135: 782–794. https://doi.org/10.1016/J.MEASUREMENT.2018.12.027
  • Donatelli M, Magarey R D, Bregaglio S, Willocquet L, Whish J P M & Savary S (2017). Modelling the impacts of pests and diseases on agricultural systems. Agricultural Systems 155: 213–224. https://doi.org/10.1016/J.AGSY.2017.01.019
  • Fulari U, Shastri R & Fulari A (2020). Leaf Disease Detection Using Machine Learning. Journal of Seybold Report 15(9): 1828–1832
  • Géron A (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (R. Roumeliotis & N. Tache, Eds.; 2nd ed.). O’Reilly Media, Inc.
  • Haque M, Marwaha S, Deb C, Nigam S & Arora A (2023). Recognition of diseases of maize crop using deep learning models. Neural Computing and Applications 35(10): 7407–7421. https://doi.org/10.1007/s00521-022-08003-9
  • Jasrotia S, Yadav J, Rajpal N, Arora M & Chaudhary J (2023). Convolutional Neural Network Based Maize Plant Disease Identification. Procedia Computer Science 218(1): 1712–1721. https://doi.org/10.1016/J.PROCS.2023.01.149
  • Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H & Wang Y (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2. https://doi.org/10.1136/svn-2017-000101
  • Mokhtar U, El-Bendary N, Hassenian A, Emary E, Mahmood M, Hefny H & Tolba M (2015). SVM-Based Detection of Tomato Leaves Diseases. In D. Filev & J (Eds.), Advances in Intelligent Systems and Computing (Vol. 323, pp. 641–652). Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_55
  • Mukhopadhyay Dr. S, Paul M, Pal R & De D (2021). Tea leaf disease detection using multi-objective image segmentation. Multimedia Tools and Applications, 80(1). https://doi.org/10.1007/s11042-020-09567-1
  • Oerke E C & Dehne H W (2004). Safeguarding production—losses in major crops and the role of crop protection. Crop Protection 23(4): 275–285. https://doi.org/10.1016/J.CROPRO.2003.10.001
  • Padol P B & Yadav A A (2016). SVM classifier based grape leaf disease detection. 2016 Conference on Advances in Signal Processing (CASP) pp. 175–179. https://doi.org/10.1109/CASP.2016.7746160
  • Panigrahi K P, Sahoo A K & Das H (2020). A CNN Approach for Corn Leaves Disease Detection to support Digital Agricultural System. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) pp. 678–683. https://doi.org/10.1109/ICOEI48184.2020.9142871
  • Ramakrishnan M & Sahaya A N A (2015). Groundnut leaf disease detection and classification by using back probagation algorithm. 2015 International Conference on Communications and Signal Processing (ICCSP) pp. 964–968. https://doi.org/10.1109/ICCSP.2015.7322641
  • Ramesh S & Vydeki D (2019). Application of machine learning in detection of blast disease in South Indian rice crops. Journal of Phytology 11(1): 31–37. https://doi.org/10.25081/jp.2019.v11.5476
  • Sibiya M & Sumbwanyambe M (2021). Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning. Pathogens 10(2): 131. https://doi.org/10.3390/pathogens10020131
  • Singh V, Varsha & Misra A K (2015). Detection of unhealthy region of plant leaves using image processing and genetic algorithm. 2015 International Conference on Advances in Computer Engineering and Applications pp. 1028–1032. https://doi.org/10.1109/ICACEA.2015.7164858
  • Subramanian M, Shanmugavadivel K & Nandhini P S (2022). On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications 34: 13951–13968. https://doi.org/10.1007/s00521-022-07246-w
  • Vasavi P, Punitha A & Rao V N T (2022). Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: A review. International Journal of Electrical and Computer Engineering 12(2): 2079–2086. https://doi.org/10.11591/ijece.v12i2.pp2079-2086
  • Waheed A, Goyal M, Gupta D, Khanna A, Hassanien A E & Pandey H M (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture 175(12): 105456. https://doi.org/10.1016/j.compag.2020.105456
  • Xu Y, Zhao B, Zhai Y, Chen Q & Zhou Y (2021). Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network. IEEE Access 9: 27959–27970. https://doi.org/10.1109/ACCESS.2021.3058267
  • Yang W, Shen P, Ye Z, Zhu Z, Xu C, Liu Y & Mei L (2023). Adversarial Training Collaborating Multi-Path Context Feature Aggregation Network for Maize Disease Density Prediction. Processes 11(4): 1132. https://doi.org/10.3390/pr11041132
  • Zhang X, Qiao Y, Meng F, Fan C & Zhang M (2018). Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access 6: 30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405
Year 2024, Volume: 30 Issue: 3, 464 - 476, 23.07.2024
https://doi.org/10.15832/ankutbd.1288298

Abstract

References

  • Chokey T & Jain S (2019). Quality Assessment of Crops using Machine Learning Techniques. 2019 Amity International Conference on Artificial Intelligence (AICAI) pp. 259–263. https://doi.org/10.1109/AICAI.2019.8701294
  • Dhingra G, Kumar V & Joshi H D (2019). A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135: 782–794. https://doi.org/10.1016/J.MEASUREMENT.2018.12.027
  • Donatelli M, Magarey R D, Bregaglio S, Willocquet L, Whish J P M & Savary S (2017). Modelling the impacts of pests and diseases on agricultural systems. Agricultural Systems 155: 213–224. https://doi.org/10.1016/J.AGSY.2017.01.019
  • Fulari U, Shastri R & Fulari A (2020). Leaf Disease Detection Using Machine Learning. Journal of Seybold Report 15(9): 1828–1832
  • Géron A (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (R. Roumeliotis & N. Tache, Eds.; 2nd ed.). O’Reilly Media, Inc.
  • Haque M, Marwaha S, Deb C, Nigam S & Arora A (2023). Recognition of diseases of maize crop using deep learning models. Neural Computing and Applications 35(10): 7407–7421. https://doi.org/10.1007/s00521-022-08003-9
  • Jasrotia S, Yadav J, Rajpal N, Arora M & Chaudhary J (2023). Convolutional Neural Network Based Maize Plant Disease Identification. Procedia Computer Science 218(1): 1712–1721. https://doi.org/10.1016/J.PROCS.2023.01.149
  • Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H & Wang Y (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2. https://doi.org/10.1136/svn-2017-000101
  • Mokhtar U, El-Bendary N, Hassenian A, Emary E, Mahmood M, Hefny H & Tolba M (2015). SVM-Based Detection of Tomato Leaves Diseases. In D. Filev & J (Eds.), Advances in Intelligent Systems and Computing (Vol. 323, pp. 641–652). Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_55
  • Mukhopadhyay Dr. S, Paul M, Pal R & De D (2021). Tea leaf disease detection using multi-objective image segmentation. Multimedia Tools and Applications, 80(1). https://doi.org/10.1007/s11042-020-09567-1
  • Oerke E C & Dehne H W (2004). Safeguarding production—losses in major crops and the role of crop protection. Crop Protection 23(4): 275–285. https://doi.org/10.1016/J.CROPRO.2003.10.001
  • Padol P B & Yadav A A (2016). SVM classifier based grape leaf disease detection. 2016 Conference on Advances in Signal Processing (CASP) pp. 175–179. https://doi.org/10.1109/CASP.2016.7746160
  • Panigrahi K P, Sahoo A K & Das H (2020). A CNN Approach for Corn Leaves Disease Detection to support Digital Agricultural System. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) pp. 678–683. https://doi.org/10.1109/ICOEI48184.2020.9142871
  • Ramakrishnan M & Sahaya A N A (2015). Groundnut leaf disease detection and classification by using back probagation algorithm. 2015 International Conference on Communications and Signal Processing (ICCSP) pp. 964–968. https://doi.org/10.1109/ICCSP.2015.7322641
  • Ramesh S & Vydeki D (2019). Application of machine learning in detection of blast disease in South Indian rice crops. Journal of Phytology 11(1): 31–37. https://doi.org/10.25081/jp.2019.v11.5476
  • Sibiya M & Sumbwanyambe M (2021). Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning. Pathogens 10(2): 131. https://doi.org/10.3390/pathogens10020131
  • Singh V, Varsha & Misra A K (2015). Detection of unhealthy region of plant leaves using image processing and genetic algorithm. 2015 International Conference on Advances in Computer Engineering and Applications pp. 1028–1032. https://doi.org/10.1109/ICACEA.2015.7164858
  • Subramanian M, Shanmugavadivel K & Nandhini P S (2022). On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications 34: 13951–13968. https://doi.org/10.1007/s00521-022-07246-w
  • Vasavi P, Punitha A & Rao V N T (2022). Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: A review. International Journal of Electrical and Computer Engineering 12(2): 2079–2086. https://doi.org/10.11591/ijece.v12i2.pp2079-2086
  • Waheed A, Goyal M, Gupta D, Khanna A, Hassanien A E & Pandey H M (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture 175(12): 105456. https://doi.org/10.1016/j.compag.2020.105456
  • Xu Y, Zhao B, Zhai Y, Chen Q & Zhou Y (2021). Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network. IEEE Access 9: 27959–27970. https://doi.org/10.1109/ACCESS.2021.3058267
  • Yang W, Shen P, Ye Z, Zhu Z, Xu C, Liu Y & Mei L (2023). Adversarial Training Collaborating Multi-Path Context Feature Aggregation Network for Maize Disease Density Prediction. Processes 11(4): 1132. https://doi.org/10.3390/pr11041132
  • Zhang X, Qiao Y, Meng F, Fan C & Zhang M (2018). Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access 6: 30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Khaled Adil Dawood Idress 0000-0002-1631-6232

Omsalma Alsadig Adam Gadalla 0000-0001-6132-4672

Y. Benal Öztekin 0000-0003-2387-2322

Geofrey Prudence Baitu 0000-0002-3243-3252

Publication Date July 23, 2024
Submission Date April 26, 2023
Acceptance Date January 15, 2024
Published in Issue Year 2024 Volume: 30 Issue: 3

Cite

APA Idress, K. A. D., Gadalla, O. A. A., Öztekin, Y. B., Baitu, G. P. (2024). Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing. Journal of Agricultural Sciences, 30(3), 464-476. https://doi.org/10.15832/ankutbd.1288298

Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).