2024, Issue 7, Volume 16

MACHINE LEARNING IN AGRICULTURE: A COMPREHENSIVE REVIEW OF CROP PRODUCTION APPLICATIONS

Nitin Karwasra and Kanishk Verma*

Department of Renewable and Bio-energy Engineering, COAE&T, CCSHAU, Hisar- 125004

Received-16.06.2024, Revised-08.07.2024, Accepted-27.07.2024

Abstract: Agriculture has been the foundation of human civilization, and this is particularly true for India, where it continues to play a crucial role in the country’s economy and society. India’s agricultural heritage forms the cornerstone of its economy and social structure, with its significance extending far beyond economic metrics. As the world’s second-largest agricultural producer, India’s farming sector, along with related industries such as forestry and fisheries, continues to be a major contributor to the nation’s GDP. The study offers a thorough examination of machine learning’s role in agriculture.

Keywords: Agriculture, Crop, Machine, Production

References

Affognon, L., Diallo, A., Diallo, C. and Ezin, E.C. (2023). A Survey on Statistical and Machine Learning Algorithms Used in Electronic Noses for Food Quality Assessment. SN Computer Science, 4, 1-18.

Google Scholar

Anwar, H., Anwar, T. and Murtaza, S. (2023). Review on food quality assessment using machine learning and electronic nose system. Biosensors and Bioelectronics. X, 14, 100365.

Google Scholar

BanuPriya, N., Tejasvi, D. and Vaishnavi, P. (2023). Crop yield prediction based on Indian agriculture using machine learning. International Research Journal of Modernization in Engineering Technology and Science, 5(5), 5138-5143.

Google Scholar

Bassine, F. Z., Epule, T. E., Kechchour, A. and Chehbouni, A. (2023). Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review. arXiv preprint arXiv:2306.04566.

Google Scholar

Cho, O.H. (2024). Machine Learning Algorithms for Early Detection of Legume Crop Disease. Legume Research: An International Journal47(3).

Google Scholar

Choudhary, S. and Saxena, B. (2023). Analysing Machine Learning based Approaches for Detecting Late Blight Disease in Potato Crop. Journal of International Academy of Physical Sciences, 27(03), 285–293.

Google Scholar

Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., Zreikat, A. I., Cina, E., Shdefat, A. and Saker, L. (2023). Crop Prediction Model Using Machine Learning Algorithms. Applied Sciences, 13(16), 9288.

Google Scholar

Feriga, M., Gracia, N.L. and Serneels, P.M. (2024). The Impact of Climate Change on Work: Lessons for Developing Countries. Policy Research Working Papers.

Google Scholar

Gopi, S. R. and Karthikeyan, M. (2023). Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning. Engineering, Technology and Applied Science Research/Engineering, Technology and Applied Science Research, 13(4), 11360–11365.

Google Scholar

Hussain, N., Sarfraz, M.S., Sattar, S. and Riaz, S. (2022). Predict the Crop-Yield Through UAV using Machine learning A Systematic Literature Review. 2022 International Conference on IT and Industrial Technologies (ICIT), 1-6.

Google Scholar

Islam, N., Rashid, M.M., Wibowo, S., Xu, C., Morshed, A., Wasimi, S.A., Moore, S.T. and Rahman, S.M. (2021). Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm. Agriculture, 11, 387.

Google Scholar

Islam, S., Hasan, M.Z. and Kabir, S.A. (2023). Smart Agriculture System for Plant Disease Detection and Irrigation Management Using Machine Learning and IoT. 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), 1-6.

Google Scholar

Jehanzaib, M., Ajmal, M., Achite, M. and Kim, T. W. (2022). Comprehensive review: Advancements in rainfall-runoff modelling for flood mitigation. Climate10(10), 147.

Google Scholar

Koblah, D., Acharya, R., Capecci, D., Dizon-Paradis, O., Tajik, S., Ganji, F., Woodard, D. and Forte, D. (2023). A survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation. ACM Transactions on Design Automation of Electronic Systems, 28(2), 1–57.

Google Scholar

Kulyal, M. and Saxena, P. (2022). Machine Learning approaches for Crop Yield Prediction: A Review. 2022 7th International Conference on Computing, Communication and Security (ICCCS), 1-7.

Google Scholar

Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J. andUwamahoro, A. (2023). Crop yield prediction using machine learning models: case of Irish potato and maize. Agriculture, 13(1), 225.

Google Scholar

Maheswari, M.P. and Ramani, R. (2023). A Comparative Study of Agricultural Crop Yield Prediction Using Machine Learning Techniques. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 1, 1428-1433.

Google Scholar

Meenal, R., Jala, P. K., Samundeswari, R. and Rajasekaran, E. (2024). Crop water management using machine learning-based evapotranspiration estimation. Journal of Applied Biology and Biotechnology, 1-6.

Google Scholar

Mekhalfa, F. and Yacef, F. (2021). Supervised learning for crop/weed classification based on color and texture features. arXiv preprint arXiv:2106.10581.

Google Scholar

Murad, N. Y., Mahmood, T., Forkan, A. R. M., Morshed, A., Jayaraman, P. P. and Siddiqui, M. S. (2023). Weed Detection Using Deep Learning: A Systematic Literature Review. Sensors (Basel, Switzerland)23(7), 3670.

Google Scholar

Naidu, S. C. and Ossome, L. (2016). Social reproduction and the agrarian question of women’s labour in India. Agrarian South – the Journal of Political Economy, 5(1), 50–76.

Google Scholar

Nautiyal, D., Sharma, V., Dangi, S. and Sharma, S. (2024). Applications of Technology Assisted Ultrasonic Repellers in Current Era of Indian Agriculture. 2024 2nd International Conference on Computer, Communication and Control (IC4), 1-6.

Google Scholar

Neupane, J. and Guo, W. (2019). Agronomic basis and strategies for precision water management: A review. Agronomy9(2), 87.

Google Scholar

Orchi, H., Sadik, M., Khaldoun, M. and Sabir, E. (2023). Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches. Agriculture, 13(2), 352.

Google Scholar

Ouhami, M., Hafiane, A., Es-saady, Y., Hajji, M.E. and Canals, R. (2021). Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research. Remote. Sens., 13, 2486.

Google Scholar

Paruchuru, M., Mavuri, S. and Jyothsna, M.K. (2020). Challenges for Economic Growth in India – A Critique. Journal of Critical Reviews, 7(07).

Google Scholar

Parvez, R. and Chowdhury, N. H. K. (2020). Weather and crop management impact on crop yield variability. Agriculture and Food Sciences Research, 7(1), 7–15.

Google Scholar

Patel, H. M. (2023). The transformative role of artificial intelligence in modern agriculture. Review of Artificial Intelligence in Education, 4(00), e014.

Google Scholar

Paudel, D., De Wit, A., Boogaard, H., Marcos, D., Osinga, S. and Athanasiadis, I. N. (2023). Interpretability of deep learning models for crop yield forecasting. Computers and Electronics in Agriculture206, 107663.

Google Scholar

Ramos, P.J., Prieto, F.A., Montoya, E.C. and Oliveros, C.E. (2017). Automatic fruit count on coffee branches using computer vision. Comput. Electron. Agric., 137, 9–22.

Google Scholar

Sagar, R., B, A., Prasad, P.S., Prajwal, R.S. and Sanjana, V. (2024). Drone Based Crop Disease Detection Using ML. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 1-8.

Google Scholar

Sahu, H., Mishra, A. P., Jakhwal, P. and Purohit, P. (2023). Threats and Challenges to Sustainable agriculture and Rural development in India: Implications for Agricultural Extension. Journal of Global Agriculture and Ecology, 15(3), 1–10.

Google Scholar

Schoppa, L., Disse, M. and Bachmair, S. (2020). Evaluating the performance of random forest for large-scale flood discharge simulation. Journal of Hydrology590, 125531.

Google Scholar

Shahi, T. B., Dahal, S., Sitaula, C., Neupane, A. and Guo, W. (2023). Deep Learning-Based Weed Detection Using UAV images: A Comparative study. Drones, 7(10), 624.

Google Scholar

Shahi, T. B., Xu, C., Neupane, A., Fleischfresser, D. B., O’Connor, D. J., Wright, G. C. and Guo, W. (2023). Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach. Electronic Research Archive, 31(6), 3343–3361.

Google Scholar

Shanthakumari, G., Vignesh, A., Harish, R. and Roshan Karthick, R. (2024). Advancements in Smart Agriculture: A Comprehensive Review of Machine Learning and IOT Approaches. 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT), 1-6.

Google Scholar

Shawon, S.M., BaruaEma, F., Mahi, A.K. and Mohsin Sarker Raihan, M. (2023). Crop Yield Prediction: Robust Machine Learning Approaches for Precision Agriculture. 2023 26th International Conference on Computer and Information Technology (ICCIT), 1-6.

Google Scholar

Shawon, S.M., BaruaEma, F., Mahi, A.K. and Mohsin Sarker Raihan, M. (2023). Crop Yield Prediction: Robust Machine Learning Approaches for Precision Agriculture. 2023 26th International Conference on Computer and Information Technology (ICCIT), 1-6.

Google Scholar

Singh, A., Jain, M. and Tripathi, A. (2023). Identification And Diagnoses of Plant Diseases in Fruit Crops Using Machine Learning Algorithms. 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 253-257.

Google Scholar

Sultan, B. (2012). Global warming threatens agricultural productivity in Africa and South Asia. Environmental Research Letters, 7(4), 041001.

Google Scholar

Swetha, T.M., Yogitha, T., Hitha, M.K., Syamanthika, P., Poorna, S.S. and Anuraj, K. (2021). IOT Based Water Management System For Crops Using Conventional Machine Learning Techniques. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-4.

Google Scholar

Tekale, S. and Singh, K.K. (2023). A Survey of Disease Detection in Cauliflower Using Machine Learning and Deep Learning Techniques. 2023 IEEE International Carnahan Conference on Security Technology (ICCST), 1-6.

Google Scholar

Thamilselvan, R., Natesan, P., Rajalaxmi, R. R., Prasanth, D. and Yuvapriya, R. (2024, April). An Analysis of Irrigation Management for Crops using Machine Learning Algorithms. In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) (pp. 394-401). IEEE.

Google Scholar

Vinu, V. and Mane, K. (2023). Soil quality monitoring and crop prediction using IOT and machine learning. International Journal for Research in Applied Science and Engineering Technology, 11(12), 1002–1005.

Google Scholar

Virnodkar, S.S., Pachghare, V.K., Patil, V.C. and Jha, S.K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture,21, 1121 – 1155.

Google Scholar

Yasmin, S. and Billah, M. (2023). Digital image processing applications in agriculture with a machine learning approach. Agricultural Science and Technology, 15(4), 12–22.

Google Scholar