Enhancing Crop Yield Prediction Using Ensemble Learning Techniques on Multi-Modal Agricultural Data
Keywords:
Ensemble Learning, Crop Yield Prediction, Multi-Modal Agricultural Data, Extreme Gradient Boosting, Predictive AccuracyAbstract
This exploration examines the viability of outfit learning methods on multi-modular horticultural data to further develop crop yield expectation exactness. Using different datasets including soil properties, weather conditions, crop wellbeing markers, and agronomic practices, we assessed four group methods: Random Forest, Gradient Boosting, Outrageous Gradient Boosting, and AdaBoost. Results exhibit that Outrageous Gradient Boosting outflanks different methods, accomplishing a Mean Squared Error (MSE) of 900, R-squared (R2) of 0.85, and Mean Absolute Error (MAE) of 22. Near examination against gauge models outlines the predominant prescient exactness and power of gathering methods. The computational proficiency of group strategies stays similar to or better than conventional models, with preparing times going from 120 to 200 seconds and memory utilization somewhere in the range of 500 and 700 MB. This study highlights the capability of outfit learning in farming navigation, offering experiences into ideal prescient displaying procedures for improving harvest yield expectation and supporting manageable agrarian practices.
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Abed, M., Imteaz, M.A. and Ahmed, A.N. 2023, "A comprehensive review of artificial intelligence-based methods for predicting pan evaporation rate", The Artificial Intelligence Review, suppl.2, vol. 56, pp. 2861-2892.
Araújo, S.O., Ricardo, S.P., Ramalho, J.C., Lidon, F. and Barata, J. 2023, "Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives", Agronomy, vol. 13, no. 12, pp. 2976.
Bahadur, F.T., Shah, S.R. and Nidamanuri, R.R. 2023, "Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modeling: a review", Environmental monitoring and assessment, vol. 195, no. 12, pp. 1502.
Beery, S.M. 2023, Where the Wild Things Are: Computer Vision for Global-Scale Biodiversity Monitoring, California Institute of Technology.
Bourechak, A., Zedadra, O., Mohamed, N.K., Guerrieri, A., Seridi, H. and Fortino, G. 2023, "At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives", Sensors, vol. 23, no. 3, pp. 1639.
Cacho, J.F., Feinstein, J., Zumpf, C.R., Hamada, Y., Lee, D.J., Namoi, N.L., Lee, D., Boersma, N.N., Heaton, E.A., Quinn, J.J. and Negri, C. 2023, "Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning", Energies, vol. 16, no. 10, pp. 4168.
Cembrowska-Lech, D., Krzemińska, A., Miller, T., Nowakowska, A., Adamski, C., Radaczyńska, M., Mikiciuk, G. and Mikiciuk, M. 2023, "An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture", Biology, vol. 12, no. 10, pp. 1298.
Chen, Y., Wang, Y., Zhang, F., Dong, Y., Song, Z. and Liu, G. 2023, "Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities", Minerals, vol. 13, no. 9, pp. 1153.
Cheng, X., Sun, Y., Zhang, W., Wang, Y., Cao, X. and Wang, Y. 2023, "Application of Deep Learning in Multitemporal Remote Sensing Image Classification", Remote Sensing, vol. 15, no. 15, pp. 3859.
Chergui, N. and Kechadi, M.T. 2022, "Data analytics for crop management: a big data view", Journal of Big Data, vol. 9, no. 1, pp. 123.
Ding, P., Li, W., Zhao, H., Zhou, G. and Cai, C. 2023, "Recognition of Tomato Leaf Diseases Based on DIMPCNET", Agronomy, vol. 13, no. 7, pp. 1812.
Sharma, A., Chaturvedi, R., Saraswat, M., & Kalra, R. (2022). Weld reliability characteristics of AISI 304L steels welded with MPAW (Micro Plasma Arc Welding). Materials Today: Proceedings, 60, 1966-1972.
Z. Wang, P. Yan and J. Li, "A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms," Applied Sciences, vol. 13, (7), pp. 4643, 2023. Available: https://www.proquest.com/scholarly-journals/survey-on-search-strategy-evolutionary-multi/docview/2799597763/se-2.
E. Romeh and S. Mirjalili, "Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration," Sensors, vol. 23, (4), pp. 2156, 2023. Available: https://www.proquest.com/scholarly-journals/multi-robot-exploration-unknown-space-using/docview/2779680966/se-2. DOI: https://doi.org/10.3390/s23042156.
H. Wang et al, "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems," Mathematical & Computational Applications, vol. 28, (1), pp. 10, 2023. Available: https://www.proquest.com/scholarly-journals/hypervolume-newton-method-constrained-multi/docview/2779574913/se-2. DOI: https://doi.org/10.3390/mca28010010.
Gaspar-Cunha et al, "Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives," Mathematical & Computational Applications, vol. 28, (1), pp. 17, 2023. Available: https://www.proquest.com/scholarly-journals/many-objectives-optimization-machine-learning/docview/2779515110/se-2. DOI: https://doi.org/10.3390/mca28010017.
Kazi, S., Khoja, S. and Daud, A. 2023, "A survey of deep learning techniques for machine reading comprehension", The Artificial Intelligence Review, suppl.2, vol. 56, pp. 2509-2569.
Meng, X., Li, C., Li, J., Li, X., Guo, F. and Xiao, Z. 2023, "YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting", Remote Sensing, vol. 15, no. 15, pp. 3770.
Mirzaei, A., Bagheri, H. and Khosravi, I. 2023, "Enhancing Crop Classification Accuracy through Synthetic SAR-Optical Data Generation Using Deep Learning", ISPRS International Journal of Geo-Information, vol. 12, no. 11, pp. 450.
Muhammad, H.T., Fatima, Z., Zardari, S. and Guerra-Zubiaga, D. 2023, "An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision", Applied Sciences, vol. 13, no. 23, pp. 12823.
Nur, A.M., Nik Norasma Che’Ya, Juraimi, A.S., Wan Fazilah, F.I., Muhammad Huzaifah, M.R., Sulaiman, N., Saberioon, M. and Nisfariza, M.N. 2021, "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?", Agriculture, vol. 11, no. 10, pp. 1004.
Papadopoulos, S., Koukiou, G. and Anastassopoulos, V. 2024, "Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review", Journal of Imaging, vol. 10, no. 1, pp. 15.
Prasitpuriprecha, C., Jantama, S.S., Preeprem, T., Pitakaso, R., Srichok, T., Khonjun, S., Weerayuth, N., Gonwirat, S., Enkvetchakul, P., Kaewta, C. and Nanthasamroeng, N. 2023, "Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System", Pharmaceuticals, vol. 16, no. 1, pp. 13.
Saad, M.K., Shafi, I., Wasi, H.B., Isabel de la, T.D., López Flores, M.A., Juan Castanedo Galán and Ashraf, I. 2023, "A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions", Land, vol. 12, no. 8, pp. 1514.
Seka, A.M., Zhang, J., Prodhan, F.A., Ayele, G.T., Finsa, M.M., Sharma, T.P.P. and Melesse, A.M. 2022, "Hydrological drought impacts on water storage variations: a focus on the role of vegetation changes in the East Africa region. A systematic review", Environmental Science and Pollution Research, vol. 29, no. 53, pp. 80237-80256.
Singh, R., Varaha Satya, B.K. and Mohammed, A.K. 2023, "Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review", Energies, vol. 16, no. 20, pp. 7156.
Sun, F., Meng, X., Zhang, Y., Wang, Y., Jiang, H. and Liu, P. 2023, "Agricultural Product Price Forecasting Methods: A Review", Agriculture, vol. 13, no. 9, pp. 1671.
Thi-Thu-Huong Le, Aji, T.P., Oktian, Y.E., Kang, H. and Kim, H. 2023, "Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review", Applied Sciences, vol. 13, no. 9, pp. 5809.
Udendhran, R. and Balamurugan, M. 2021, "RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications", Complex and Intelligent Systems, vol. 7, no. 2, pp. 659-666.
Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022
Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219
Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729
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