Integrated Ensemble Learning Techniques for Precision Crop Yield Prediction
DOI:
https://doi.org/10.32628/CSEIT25113391Keywords:
Agriculture Data, Image Processing, Crop Disease Detection, Pattern Recognition, Crop Yield Prediction, Ensemble Learning, Random Forest RegressorAbstract
Agriculture is an essential element of human civilization. It bolsters the economy while also offering nourishment. Plant leaves and crops are vulnerable to several diseases in agricultural practices. The agricultural sector's growing dependence on technology has facilitated the development of sophisticated data-driven approaches, with crop yield prediction becoming a pivotal emphasis. Precise crop production forecasting is crucial for strategic planning, optimal resource distribution, and enhancing agricultural output. Forecasting crop yields continues to be a significant difficulty in agriculture, since these predictions impact decision-making on global, regional, and individual crop scales. Historically, such forecasts have used several data sources, including agricultural, land, meteorological, atmospheric, and other relevant information. This research presents an innovative method for forecasting agricultural production with an ensemble learning model. The suggested system employs historical agricultural data, including district, crop year, season, and area. In recent years, the use of machine learning methods has become a significant analytical method for assessing agricultural production, therefore guiding choices related to crop selection and management strategies across the whole growing season. Diverse machine learning algorithms have been used in studies to predict agricultural yields. Our research presents a stacked ensemble model aimed at forecasting crop production. This study presents a stacking ensemble model that incorporates K-Nearest Neighbours Regressor, Random Forest Regressor, and Multiple Linear Regressor as base learners, with Decision Tree Regressor serving as the meta-learner. This ensemble method improves predictive performance by capitalizing on the strengths of each individual model while mitigating their flaws. This suggested system provides robust decision-making assistance for farmers and agricultural stakeholders, enabling them to make educated, data-driven decisions that improve sustainability and efficiency in agriculture.
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Wang, Z.; Zhang, C.; Gao, L.; Fan, C.; Xu, X.; Zhang, F.; Zhou, Y.; Niu, F.; Li, Z. Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index. Remote Sens. 2024, 16, 1995
Chen, P.; Li, Y.; Liu, X.; Tian, Y.; Zhu, Y.; Cao,W.; Cao, Q. Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: A case study in Jiangsu Province, China. Comput. Electron. Agric. 2023, 213, 108201
Patil V. H. ., Ghaly M. A. ., Yusuf Perwej, Shrivastava A., “A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference and IoT” , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), SCOPUS, ISSN: 2321-8169, Volume 11, Issue 9, Pages 62-68, September 2023, 10.17762/ijritcc.v11i9.8320
Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
Venkata K. S. Maddala, Dr. Shantanu Shahi, Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
KDV Prasad, Yusuf Perwej, E. Nageswara Rao, Himanshu Bhaidas Patel, “IoT Devices for Agricultural to Improve Food and Farming Technology”, Journal of Survey in Fisheries Sciences (JSFS), ISSN: 2368-7487, SCOPUS, Vol. 10, No. 1S (2023): Special Issue 1, Pages 4054-4069, Canada, March 2023
Patil V. H. ., Dhanke J. ., Ghaly M. A. ., Patil, R. N. ., Yusuf Perwej, Shrivastava A., “A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference and IoT” , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), SCOPUS, ISSN: 2321-8169, Volume 11, Issue 9, Pages 62-68, 2023, DOI: 10.17762/ijritcc.v11i9.8320
Fei, S.; Hassan, M.A.; Xiao, Y.; Su, X.; Chen, Z.; Cheng, Q.; Duan, F.; Chen, R.; Ma, Y. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precis. Agric. 2023, 24, 187–212
Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
van Klompenburg, T.; Kassahun, A.; Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 2020, 177, 105709
N. Akhtar, Hemlata Pant, Apoorva Dwivedi, Vivek Jain, Y. Perwej, “A Breast Cancer Diagnosis Framework Based on Machine Learning”, International Journal of Scientific Research in Science, Engineering and Technology, Print ISSN: 2395-1990, Online ISSN: 2394-4099, Volume 10, Issue 3, Pages 118-132, 2023, DOI: 10.32628/IJSRSET2310375
Chlingaryan, A.; Sukkarieh, S.; Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 2018, 151, 61–69
Wang X et al (2020) Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches. Remote Sens12(11):1744.
A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review," Computers and Electronics in Agriculture, vol. 151, pp. 61-69, 2018.
T. K. Fegade and B. V. Pawar, "Crop prediction using artificial neural network and support vector machine," Data Management, Analytics and Innovation, Springer, Berlin, Germany, pp. 311-324, 2020.
Sandeep Gupta, Angelina Geetha, K. Sakthidasan Sankaran, Abu Sarwar Zamani, Mahyudin Ritonga, Roop Raj, Samrat Ray, Hussien Sobahi Mohammed, "Machine Learning- and Feature Selection- Enabled Framework for Accurate Crop Yield Prediction", Journal of Food Quality, vol. 2022, Article ID 6293985, 7 pages, 2022. https://doi.org/10.1155/2022/6293985.
Alberto Gonzalez-Sanchez, Juan Frausto-Solis, Waldo Ojeda- Bustamante, "Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction", The Scientific World Journal, vol. 2014, Article ID 509429, 10 pages, 2014. https://doi.org/10.1155/2014/509429.
Woittiez, L. S., Van Wijk, M. T., Slingerland, M., Van Noordwijk, M., & Giller, K. E. (2017). Yield gaps in oil palm: A quantitative review of contributing factors. European Journal of Agronomy, 83, 57-77. https://doi.org/10.1016/j.eja.2016.11.002
Kajal, Neha Singh, Nikhat Akhtar, Ms. Sana Rabbani, Yusuf Perwej, Susheel Kumar, “Using Emerging Deep Convolutional Neural Networks (DCNN) Learning Techniques for Detecting Phony News”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 10, Issue 1, Pages 122-137, 2024, DOI: 10.32628/CSEIT2410113
N.Akhtar, Kumar Bibhuti B. Singh, Devendra Agarwal, Y. Perwej, “Improving Quality of Life with Emerging AI and IoT Based Healthcare Monitoring Systems”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, ISSN: 2456-3307, Volume 11, Issue 1, Pages 96-107, January 2025, DOI: 10.32628/CSEIT2514551
Y. Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), which is published by Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
Y. Perwej, “Recurrent Neural Network Method in Arabic Words Recognition System”, International Journal of Computer Science and Telecommunications (IJCST), which is published by Sysbase Solution (Ltd), UK, London, (http://www.ijcst.org) , ISSN 2047-3338, Volume 3, Issue 11, Pages 43-48, 2012
Oikonomidis A, CatalCand Kassahun A 2022 Hybrid deep learning-based models for crop yield prediction Appl. Artif. Intell. 36 2031822
Kamath P, Patil P, Shrilatha S and Sowmya S 2021 Crop yield forecasting using data mining Global Transitions Proceedings 2 402–7
Hasan M, MarjanMA, UddinMP, AfjalMI, Kardy S,MaS andNamY 2023 Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation Frontiers in Plant Science 14 1234555
Boppudi S 2024 Deep ensemble model with hybrid intelligence technique for crop yield prediction Multimedia Tools Appl. 1–21
Kumar Bibhuti B. Singh, N. Akhtar, Devendra Agarwal, Susheel Kumar, Y. Perwej, “An Evaluation of OpenCV's Investigation into Hand Gesture Recognition Methods”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990, Online ISSN: 2394-4099, Volume 12, Issue 1, Pages 01-14, January 2025, DOI: 10.32628/IJSRSET25121150
Sadenova, M., Beisekenov, N., Varbanov, P.S. and Pan, T., 2023. Application of machine learning and neural networks to predict the yield of cereals, legumes, oilseeds and forage crops in Kazakhstan. Agriculture, 13(6), p.1195.
Chlingaryan, A.; Sukkarieh, S.; Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 2018, 151, 61–69.
Marques Ramos, A.P.; Prado Osco, L.; Elis Garcia Furuya, D.; Nunes Goncalves,W.; Cordeiro Santana, D.; Pereira Ribeiro Teodoro, L.; Antonio da Silva Junior, C.; Fernando Capristo-Silva, G.; Li, J.; Henrique Rojo Baio, F.; et al. A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Comput. Electron. Agric. 2020, 178, 105791
Ren, P.; Li, H.; Han, S.; Chen, R.; Yang, G.; Yang, H.; Feng, H.; Zhao, C. Estimation of Soybean Yield by Combining Maturity Group Information and Unmanned Aerial Vehicle Multi-Sensor Data Using Machine Learning. Remote Sens. 2023, 15, 4286.
Ji, Y.; Chen, Z.; Cheng, Q.; Liu, R.; Li, M.; Yan, X.; Li, G.; Wang, D.; Fu, L.; Ma, Y.; et al. Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.). Plant Methods 2022, 18, 26
Sunny Kumar, Apoorva Dwivedi, Yusuf Perwej, Moazzam Haidari, Siddharth Singh, Dr. Nagarajan Gurusamy, “A Smart IoT-Image Processing System for Real-Time Skin Cancer Detection”, Journal of Neonatal Surgery (JNS), SCOPUS, ISSN: 2226-0439 (Online), Volume 14, Issue S14, Pages 823-831, April 2025, DOI: 10.52783/jns.v14.4330
Yang, Q.; Shi, L.; Han, J.; Zha, Y.; Zhu, P. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Res. 2019, 235, 142–153.
Zhang, Y.; Liu, J.; Shen,W. A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications. Appl. Sci. 2022, 12, 8654
Anjali Yadav, Shruti Dwivedi, Anubhav Dwivedi, Ujjwal Thakur, Dr. Nikhat Akhtar, “Intelligent Disease Diagnosis: A Multi-Disease Prediction Approach Using Machine Learning”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990, Online ISSN: 2394-4099, Volume 12, No. 3, Pages 98 -109, May 2025, DOI: 10.32628/IJSRSET251235
Amanullah Ansari, Shrejal Singh, Dr. Nikhat Akhtar, “AI-Driven Crop Disease Detection and Management in Smart Agriculture”, International Journal of Scientific Research in Science and Technology (IJSRST), SSN: 2395-6011, Volume 12, Issue 3, Pages 309-319, May 2025, DOI: 10.32628/IJSRST2512341
Apoorva Dwivedi, K. Manivannan, Sunny Kumar, Neha Anand, Dr. Yusuf Perwej, Rakhi Kamra, “A Real-Time Environmental Pollution Monitoring Framework Using IoT and Remote Sensing Technologies”, International Journal of Environmental Sciences (IJES), SCOPUS, ISSN: 2229-7359, Volume 11, Number 7s, Pages 1064 - 1075, June 2025
Li, D.; Miao, Y.; Gupta, S.K.; Rosen, C.J.; Yuan, F.; Wang, C.; Wang, L.; Huang, Y. Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning. Remote Sens. 2021, 13, 3322
Y. Perwej, Nikhat Akhtar, Devendra Agarwal, “The emerging technologies of Artificial Intelligence of Things (AIoT) current scenario, challenges, and opportunities”, Book Title “Convergence of Artificial Intelligence and Internet of Things for Industrial Automation”, SCOPUS, ISBN: 978-1-032-42844-4, CRC Press, Taylor & Francis Group, 2024 Link:https://www.taylorfrancis.com/chapters/edit/10.1201/9781003509240-1/emerging-technologiesartificial-intelligence-things-aiot-current-scenario-challenges-opportunities-yusuf-perwej-nikhatakhtar-devendra-agarwal?context=ubx&refId=537f1a8f-6a94-4439-b337-3ad3d1ce8845, DOI: 10.1201/9781003509240-1
Marshall, M., M. Belgiu, M. Boschetti, M. Pepe, A. Stein, and A. Nelson. 2022. “Field-Level Crop Yield Estimation with PRISMA and Sentinel-2.” ISPRS Journal of Photogrammetry and Remote Sensing 187:191–210
Paudel, D., D. Marcos, A. de Wit, H. Boogaard, and I. N. Athanasiadis. 2023. “A Weakly Supervised Framework for High-Resolution Crop Yield Forecasts.” Environmental Research Letters 18 (9): 094062. Publisher: IOP Publishing
Alkhammash EH, HadjouniMand Elshewey AM2022 Ahybrid ensemble stacking model for gender voice recognition approach Electronics 11 1750
N. Akhtar, Nazia Tabassum, Asif Perwej, Y. Perwej,“ Data Analytics and Visualization Using Tableau Utilitarian for COVID-19 (Coronavirus)”, Global Journal of Engineering and Technology Advances (GJETA), ISSN : 2582-5003, Volume 3, Issue 2, Pages 28-50, 2020, DOI: 10.30574/gjeta.2020.3.2.0029
Mahmoud AbouGhaly, Y. Perwej, Mumdouh Mirghani Mohamed Hassan, Nikhat Akhtar, “Smart Sensors and Intelligent Systems: Applications in Engineering Monitoring” , International Journal of Intelligent Systems and Applications in Engineering, SCOPUS, ISSN: 2147- 6799, Volume 12, Issue 22s, Pages 720–727, July 2024
Anmol Chauhan, Ms. Sana Rabbani, Devendra Agarwal, Nikhat Akhtar, Yusuf Perwej, “Diffusion Dynamics Applied with Novel Methodologies”, International Journal of Innovative Research in Computer Science and Technology (IJIRCST), ISSN (Online): 2347-5552, Volume-12, Issue-4, Pages 52 - 58, July 2024, DOI: 10.55524/ijircst.2024.12.4.9
M. Claverie, L. Nisini, S. Janssen, S. Osinga, and I. N. Athanasiadis. 2022. “Machine Learning for Regional Crop Yield Forecasting in Europe.” Field Crops Research 276:108377
Ozaki, V. A., S. K. Ghosh, B. K. Goodwin, and R. Shirota. 2008. “Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts.” American Journal of Agricultural Economics 90 (4): 951–961
KDV Prasad, Yusuf Perwej, E. Nageswara Rao, Himanshu Bhaidas Patel, “IoT Devices for Agricultural to Improve Food and Farming Technology”, Journal of Survey in Fisheries Sciences (JSFS), ISSN: 2368-7487, SCOPUS, Volume 10, No. 1S (2023): Special Issue 1, Pages 4054-4069, Canada, 2023
Kim, K.-H., Y. Doi, N. Ramankutty, and T. Iizumi. 2021. “A Review of Global Gridded Cropping System Data Products.” Environmental Research Letters 16 (9): 093005
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