Machine Learning based Disease and Pest detection in Agricultural Crops
DOI:
https://doi.org/10.4108/eetiot.5049Keywords:
Machine Learning, Smart Agriculture, Disease Diagnosis, Pest Detection, Artificial IntelligenceAbstract
INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions.
OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study.
METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61
CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.
Downloads
References
Jibran and Mufti, v: Issues and challenges in Indian agriculture. International Journal of Commerce and Business Management. Oct 2019; vol. 12, no. 2. pp. 85–88. DOI: https://doi.org/10.15740/HAS/IJCBM/12.2/85-88
Talaviya, T, Shah, D, Patel, N, Yagnik, H, and Shah, M: Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture.2020; vol. 4, pp. 58–73. DOI: https://doi.org/10.1016/j.aiia.2020.04.002
Akhter, R and Sofi, S. A: Precision agriculture using IOT data analytics and machine learning. Journal of King Saud University -Computer and Information Sciences. 2021; vol. 34, no. 8.pp. 5602–5618. DOI: https://doi.org/10.1016/j.jksuci.2021.05.013
Gupta, R, Sharma, A. K, Garg, O, Modi, K, Kasim, S, et al: WB-CPI: Weather based crop prediction in India using Big Data Analytics. IEEE Access. 2021; vol. 9, pp. 137869–137885. DOI: https://doi.org/10.1109/ACCESS.2021.3117247
Yadav, N, Alfayeed, S. M, and Wadhawan, A: Machine learning in agriculture: Techniques and applications. International Journal of Engineering Applied Sciences and Technology. Dec 2020; vol. 5, no. 7. pp. 118–122. DOI: https://doi.org/10.33564/IJEAST.2020.v05i07.018
Raja Madhu Shaker, B, Hemantha Kumar, J, Chaitanya, V, Sriranjitha, P, Ravi Kumar, K, and Jagan Mohan Rao, P, et al: Economics of Chilli Cultivation in Khammam District of Telangana. International Journal of Current Microbiology and Applied Sciences. 2021; vol. 10, no. 2. pp. 893–901. DOI: https://doi.org/10.20546/ijcmas.2021.1002.105
Bhatt, B, and Karnatak, A. K: Seasonal incidence of major insect pests of chilli crop and their correlation with abiotic factors. International Journal of Chemical Studies. Feb 2020; vol. 8, no. 2 pp. 1837–1841. DOI: https://doi.org/10.22271/chemi.2020.v8.i2ab.9028
Khalid, S A. N, Mohamad Roff, M. N, Touhidor, M. R, and Idris, A. B: Effects of plant height, maturity and climatic factors on the population of whitefly (Bemisia tabaci) on chilli (Capsicum annuum L.). Journal of Tropical Agriculture and Food Science.2006; vol. 34, no. 1. pp. 195–206.
Kowshika, N, Panneerselvam, S, Geethalakshmi, V, Arumugam, T and Jagadeeswaran, R: Performance of rainfed chilli crop in Tamil Nadu under climate change in RCP4.5. Journal of Agrometeorology. 2021; vol. 23, no. 3. pp. 324–329. DOI: https://doi.org/10.54386/jam.v23i3.37
Keerthi, M, Swamy Das, M, Srujana I: Disease detection and remote monitoring in chilli crop using image processing. International Journal for Research in Applied Science and Engineering Technology. Aug 2021; vol. 9, no. 8.pp. 2988–2995. DOI: https://doi.org/10.22214/ijraset.2021.37843
Vanitha, C. N, Archana, N, and Sowmiya, R: Agriculture analysis using data mining and Machine Learning Techniques.2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). Mar 2019. DOI: https://doi.org/10.1109/ICACCS.2019.8728382
Arjun, K.M: Indian Agriculture Status, Importance and Role in Indian Economy. International Journal of Agriculture and Food Science Technology.2013; vol. 4, no. 4.pp. 343–346.
Balasubramaniam, S., & Bharathi, R.: Performance analysis of parallel FIR digital filter using VHDL. International Journal of Computer Applications.2012;39(9). 1-6. DOI: https://doi.org/10.5120/4845-7109
Poonia, R., Gao, X. Z, Raja, L, Sharma, S, and Vyas, S: Smart Farming Technologies for Sustainable Agricultural Development. Hershey, PA, USA: IGI Global.2019 DOI: https://doi.org/10.4018/978-1-5225-5909-2
Varshney, D, Babukhanwala, B, Khan, J, Saxena, D, and Singh, A. K: Plant Disease Detection Using Machine Learning Techniques.2022 3rd International Conference for Emerging Technology (INCET). Jul 2022; pp. 1–5. DOI: https://doi.org/10.1109/INCET54531.2022.9824653
Gosai, D, Kaka, B, Garg, D, Patel, R, and Ganatra, A: Plant disease detection and classification using machine learning algorithm. 2022 International Conference for Advancement in Technology (ICONAT). Jan. 2022; pp1-6 DOI: https://doi.org/10.1109/ICONAT53423.2022.9726036
Ramesh, S, Ramachandra, H, Niveditha, M, Pooja, R, Prasad bhat, N, et al: Plant disease detection using machine learning.2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C Apr 2018; pp. 41–45. DOI: https://doi.org/10.1109/ICDI3C.2018.00017
Liakos, K, Busato, P, Moshou, D, Pearson, S, and Bochtis, D: Machine learning in agriculture: A Review. Sensors, Vol. 18, no. 8, p. 2674. DOI: https://doi.org/10.3390/s18082674
Parhyar, R. A, Mari, J. M, Bukero, A, Lanjar, A. G, Hyder, M, Khan, N, et al: Relative efficacy of synthetic insecticides against sucking insect pests of chilli crop. Pure and Applied Biology, vol. 8, no. 4. pp. 2248–2256. DOI: https://doi.org/10.19045/bspab.2019.80170
Syed, S. F, Varghese, D, and Tripathy, v.: Remote sensor networks for chilli crop disease prediction using thermal image processing techniques. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). pp. 38–43, Apr 2020. DOI: https://doi.org/10.1109/CSCITA47329.2020.9137780
Balasubramaniam, S., & Gollagi, S. G, Software defect prediction via optimal trained convolutional neural network. Advances in Engineering Software, 169, 103138, (2022). DOI: https://doi.org/10.1016/j.advengsoft.2022.103138
Balasubramaniam S, C. Vijesh Joe, Chinnadurai Manthiramoorthy, K. Satheesh Kumar, ReliefF based feature selection and Gradient Squirrel Search Algorithm enabled Deep Maxout Network for detection of heart disease,Biomedical Signal Processing and Control 2024, 87 (105446), 1-8 DOI: https://doi.org/10.1016/j.bspc.2023.105446
High Level Expert Forum - How to Feed the World in 2050 [online]. https://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf. (Accessed on Feb 2023).
Balasubramaniam, S, & Kavitha, V: Hybrid Security Architecture for Personal Health Record Transactions in Cloud Computing. Advances in Information Sciences and Service Sciences. 2015;7(1),121. DOI: https://doi.org/10.1155/2015/927867
Devojee, B.S, Meena, S, Sharma, A. K, Agarwal, C: Performance evaluation of weeder by number of blades per flange in chilli crop. International Journal of Chemical Studies. Mar 2020; vol. 8, no. 2. pp. 727–731. DOI: https://doi.org/10.22271/chemi.2020.v8.i2k.8855
Muneeswari, G., S. Sajithra Varun, Ramakrishna Hegde, S. Sharon Priya, P. Josephin Shermila, and A. Prasanth. "Self-diagnosis platform via IOT-based privacy preserving medical data." Measurement: Sensors 25, 2023: 100636. DOI: https://doi.org/10.1016/j.measen.2022.100636
Balasubramaniam, S, & Kumar, K. S.: Optimal Ensemble learning model for COVID-19 detection using chest X-ray images. Biomedical Signal Processing and Control.2023; 81, 104392. DOI: https://doi.org/10.1016/j.bspc.2022.104392
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
