MACHINE LEARNING IN AGRICULTURE FOR CROP DISEASES IDENTIFICATION: A SURVEY
DOI:
https://doi.org/10.29121/granthaalayah.v11.i3.2023.5099Keywords:
Agriculture, Classification of Crop, Crop Diseases Detection, Disease in Agriculture, Farming, Leaf Disease, Pest Disease Identification.Abstract [English]
The field of computer science known as machine learning is used to create algorithms that have the ability to self-learn or learn on their own. This is how the phrase "Machine Learning" came to be. Artificial intelligence in-cludes a subfield called machine learning. These days, machine learning and deep learning techniques are frequently used to classify and recognize leaf diseases. Recognizing leaf disease at an early stage is crucial in agricultural fields for all crops. Accurate disease detection at an early stage helps farmers boost production and their economy. The suggested study is a survey of more than 40 research papers that classify and identify plant leaf diseases using various machine learning and deep learning algorithms. It also discuss-es machine learning, its application to agriculture, as well as its benefits and drawbacks. Develop an automatic disease detection system for leaf disease classification and detection using web-based or mobile-based applications for future work. Using this survey to build a more accurate model for leaf disease classification and detection using machine learning with a wide range of datasets. This will be very beneficial for farmers to boost productivity and build their economies.
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Abade, A., Ferreira, P. A., & de Barros Vidal, F. (2021). Plant Diseases Recognition on Images Using Convolutional Neural Networks: A Systematic Review. Computers and Electronics in Agriculture, 185. https://doi.org/10.1016/j.compag.2021.106125 DOI: https://doi.org/10.1016/j.compag.2021.106125
Agarwal, M., Gupta, S. K., & Biswas, K. K. (2020). Development of an Efficient CNN Model for Tomato Crop Disease Identification. Sustainable Computing: Informatics and Systems, 28. https://doi.org/10.1016/j.suscom.2020.100407 DOI: https://doi.org/10.1016/j.suscom.2020.100407
Ahmad, J., Jan, B., Farman, H., Ahmad, W., & Ullah, A. (2020). Disease Detection in Plum Using Convolutional Neural Network Under True Field Conditions. Sensors (Switzerland), 20(19), 1-18. https://doi.org/10.3390/s20195569 DOI: https://doi.org/10.3390/s20195569
Arnal Barbedo, J. G. (2019). Plant Disease Identification from Individual Lesions And Spots Using Deep Learning. Biosystems Engineering, 180, 96-107. https://doi.org/10.1016/j.biosystemseng.2019.02.002 DOI: https://doi.org/10.1016/j.biosystemseng.2019.02.002
Asad, M. H., & Bais, A. (2020). Weed Detection in Canola Fields Using Maximum Likelihood Classification and Deep Convolutional Neural Network. Information Processing in Agriculture, 7(4), 535-545. https://doi.org/10.1016/j.inpa.2019.12.002 DOI: https://doi.org/10.1016/j.inpa.2019.12.002
Ashwinkumar, S., Rajagopal, S., Manimaran, V., & Jegajothi, B. (2021). Automated Plant Leaf Disease Detection and Classification Using Optimal Mobilenet-Based Convolutional Neural Networks. Materials Today: Proceedings, 51, 480-487. https://doi.org/10.1016/j.matpr.2021.05.584 DOI: https://doi.org/10.1016/j.matpr.2021.05.584
Bajait, V., & Malarvizhi, N. (2020). Review on Different Approaches for Crop Prediction and Disease Monitoring Techniques. Proceedings of the 4th International Conference on Elec-tronics, Communication and Aerospace Technology, ICECA 2020, 1244-1249. https://doi.org/10.1109/ICECA49313.2020.9297474 DOI: https://doi.org/10.1109/ICECA49313.2020.9297474
Caldeira, R. F., Santiago, W. E., & Teruel, B. (2021). Identification of Cotton Leaf Lesions Using Deep Learning Techniques. Sensors, 21(9). https://doi.org/10.3390/s21093169 DOI: https://doi.org/10.3390/s21093169
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Ayari, M. A., Khan, A. U., Khan, M. S., Al-Emadi, N., Reaz, M. B. I., Islam, M. T., & Ali, S. H. M. (2021). Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques. AgriEngineering, 3(2), 294-312. https://doi.org/10.3390/agriengineering3020020 DOI: https://doi.org/10.3390/agriengineering3020020
Da Silva Abade, A., de Almeida, A. P. G. S., & de Barros Vidal, F. (2019). Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks. VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 5, 450-458. https://doi.org/10.5220/0007383904500458 DOI: https://doi.org/10.5220/0007383904500458
Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72-80. https://doi.org/10.1016/j.biosystemseng.2016.08.024 DOI: https://doi.org/10.1016/j.biosystemseng.2016.08.024
Ferentinos, K. P. (2018). Deep Learning Models for Plant Disease Detection and Diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009 DOI: https://doi.org/10.1016/j.compag.2018.01.009
Ghosh, S., Chakraborty, A., Bandyopadhyay, A., Kundu, I., & Sabut, S. (2021a). Detecting Diseased Leaves Using Deep Learning. Lecture Notes in Electrical Engineering, 728 LNEE, 41-46. https://doi.org/10.1007/978-981-33-4866-0_6
Ghosh, S., Chakraborty, A., Bandyopadhyay, A., Kundu, I., & Sabut, S. (2021b). Detecting Diseased Leaves Using Deep Learning. Lecture Notes in Electrical Engineering, 728 LNEE, 41-46. https://doi.org/10.1007/978-981-33-4866-0_6 DOI: https://doi.org/10.1007/978-981-33-4866-0_6
Hang, J., Zhang, D., Chen, P., Zhang, J., & Wang, B. (2019). Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network. Sensors (Switzerland), 19(19). https://doi.org/10.3390/s19194161 DOI: https://doi.org/10.3390/s19194161
Kaleem, M. K., Purohit, N., Azezew, K., & Asemie D A Assistant, S. (2021a). A Modern Approach for Detection of Leaf Diseases Using Image Processing and ML Based SVM Classifier. In Turkish Journal of Computer and Mathematics Education 12(13).
Kaleem, M. K., Purohit, N., Azezew, K., & Asemie D A Assistant, S. (2021b). A Modern Approach for Detection of Leaf Diseases Using Image Processing and ML Based SVM Classifier. In Turkish Journal of Computer and Mathematics Education 12 (13).
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for Disease Detection in Tomato Leaves. Applied Soft Com-puting Journal, 86. https://doi.org/10.1016/j.asoc.2019.105933 DOI: https://doi.org/10.1016/j.asoc.2019.105933
Krishnaswamy Rangarajan, A., & Purushothaman, R. (2020). Disease Classification in Egg-plant Using Pre-trained VGG16 and MSVM. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59108-x DOI: https://doi.org/10.1038/s41598-020-59108-x
Manjula, K., Spoorthi, S., Yashaswini, R., & Sharma, D. (2022). Plant Disease Detection Using Deep Learning. Lecture Notes in Electrical Engineering, 783(May), 1389-1396. https://doi.org/10.1007/978-981-16-3690-5_133 DOI: https://doi.org/10.1007/978-981-16-3690-5_133
Nigam, S., Jain, R., Marwaha, S., & Arora, A. (2021a). 12 Wheat Rust Disease Identification Using Deep Learning. In the Internet of Things and Machine Learning in Agriculture, De Gruyter, 239-250. https://doi.org/10.1515/9783110691276-012
Nigam, S., Jain, R., Marwaha, S., & Arora, A. (2021b). 12 Wheat Rust Disease Identification Using Deep Learning. In Internet of Things and Machine Learning in Agriculture 239-250. De Gruyter. https://doi.org/10.1515/9783110691276-012 DOI: https://doi.org/10.1515/9783110691276-012
Patil, B. (2021). A Perspective View of Cotton Leaf Image Classification Using Machine Learn-ing Algorithms Using WEKA. https://doi.org/10.21203/rs.3.rs-502091/v1 DOI: https://doi.org/10.21203/rs.3.rs-502091/v1
Patil, B. M., & Burkpalli, V. (2021). A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA. Advances in Human-Computer Interac-tion, 2021, 1-15. https://doi.org/10.1155/2021/9367778 DOI: https://doi.org/10.1155/2021/9367778
Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019a). Deep Convolutional Neural Networks For Mobile Capture Device-Based Crop Disease Classification in the Wild. Computers and Electronics in Agriculture, 161, 280-290. https://doi.org/10.1016/j.compag.2018.04.002
Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019b). Deep convolutional Neural Networks for Mobile Capture Device-Based Crop Disease Classification in the Wild. Computers and Electronics in Agriculture, 161, 280-290. https://doi.org/10.1016/j.compag.2018.04.002 DOI: https://doi.org/10.1016/j.compag.2018.04.002
Raghavendra, Y., & Sathish Kumar, G. A. E. (2021a). Multivariant Disease Detection from Different Plant Leaves and Classification using Multiclass Support Vector Machine. In Turk-ish Journal of Computer and Mathematics Education 12(13).
Raghavendra, Y., & Sathish Kumar, G. A. E. (2021b). Multivariant Disease Detection from Different Plant Leaves and Classification using Multiclass Support Vector Machine. In Turk-ish Journal of Computer and Mathematics Education 12(13).
Ramesh, S., & Vydeki, D. (2018). Rice Blast Disease Detection and Classification using a Machine Learning Algorithm. Proceedings - 2nd International Conference on Micro-Electronics and Telecommunication Engineering, ICMETE 2018, 255-259. https://doi.org/10.1109/ICMETE.2018.00063 DOI: https://doi.org/10.1109/ICMETE.2018.00063
Rubini, P. E., & Kavitha, P. (2021). The Deep Learning Model for Early Prediction of Plant Disease. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 1104-1107. https://doi.org/10.1109/ICICV50876.2021.9388538 DOI: https://doi.org/10.1109/ICICV50876.2021.9388538
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019a). Plant Disease Detection and Classification by Deep Learning. in Plants 8(11). MDPI AG. https://doi.org/10.3390/plants8110468
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019b). Plant Disease Detection and Classification by Deep Learning. in Plants 8(11). MDPI AG. https://doi.org/10.3390/plants8110468 DOI: https://doi.org/10.3390/plants8110468
Singh, V. (2019). Sunflower Leaf Disease Detection Using Image Segmentation Based on Parti-Cle Swarm Optimization. Artificial Intelligence in Agriculture, 3, 62-68. https://doi.org/10.1016/j.aiia.2019.09.002 DOI: https://doi.org/10.1016/j.aiia.2019.09.002
Singh, V., Sharma, N., & Singh, S. (2020). A review of Imaging Techniques for Plant Disease Detection. Artificial Intelligence in Agriculture, 4, 229-242. https://doi.org/10.1016/j.aiia.2020.10.002 DOI: https://doi.org/10.1016/j.aiia.2020.10.002
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016. https://doi.org/10.1155/2016/3289801 DOI: https://doi.org/10.1155/2016/3289801
Sri Eshwar College of Engineering, & Institute of Electrical and Electronics Engineers. (n.d.). 2020 6th International Conference of Advanced Computing and Communication Systems (ICACCS).
Tulshan, A. S. (2019). Plant Leaf Disease Detection using Machine Learning. https://ieeexplore.ieee.org/abstract/document/8944556 DOI: https://doi.org/10.1109/ICCCNT45670.2019.8944556
Ullah, M. R., Dola, N. A., Sattar, A., & Hasnat, A. (2020a). Plant Diseases Recognition Using Machine Learning. Proceedings of the 2019 8th International Conference on System Model-ing and Advancement in Research Trends, SMART 2019, 67-73. https://doi.org/10.1109/SMART46866.2019.9117284
Ullah, M. R., Dola, N. A., Sattar, A., & Hasnat, A. (2020b). Plant Diseases Recognition Using Machine Learning. Proceedings of the 2019 8th International Conference on System Model-ing and Advancement in Research Trends, SMART 2019, 67-73. https://doi.org/10.1109/SMART46866.2019.9117284 DOI: https://doi.org/10.1109/SMART46866.2019.9117284
Xian, T. S., & Ngadiran, R. (2021). Plant Diseases Classification using Machine Learning. Journal of Physics: Conference Series, 1962(1). https://doi.org/10.1088/1742-6596/1962/1/012024 DOI: https://doi.org/10.1088/1742-6596/1962/1/012024
Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. (2020). GANS-based data augmentation for citrus disease severity detection using deep learning. IEEE Access, 8, 172882-172891. https://doi.org/10.1109/ACCESS.2020.3025196 DOI: https://doi.org/10.1109/ACCESS.2020.3025196
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