ENHANCING TOMATO VARIETY SELECTION AND YIELD PREDICTION MODEL USING ADVANCED MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.2371Keywords:
Crop Yield Prediction, Spatial Data, Climate Data, Missing Value, PCAAbstract [English]
One important agricultural practice that greatly contributes to the world's food supply is the growing of tomatoes. However, choosing the right tomato varieties and accurately estimating their yields are difficult undertakings that depend on several variables, such as crop statistics, climate, and geography. Advanced machine learning approaches can be used to improve prediction accuracy, feature selection, and preprocessing to overcome these problems. To optimize tomato variety selection and yield prediction, this work investigates an integrated approach that uses improved preprocessing approaches for outlier and missing information, an advanced feature selection method, and a hybrid algorithm.
References
Banshilal, Patidar, -., Samarth, Sinha., Anurag, Jain. (2024). Crop yield prediction using automated analytical and predictive techniques. doi: 10.1201/9781032700502-70 DOI: https://doi.org/10.1201/9781032700502-70
Shalini, Bhadola., Kavita, Rathi. (2024). Application of machine learning for crop yield prediction challenges and future directions. doi: 10.58532/v3bdai2p3ch3 DOI: https://doi.org/10.58532/V3BDAI2P3CH3
Ramakrishnan, Raman., Hanumaji, Kantari., Atul, A, Gokhale., K., Elangovan., B., Meenakshi., S., Srinivasan. (2024). Agriculture Yield Estimation Using Machine Learning Algorithms. doi: 10.1109/autocom60220.2024.10486107 DOI: https://doi.org/10.1109/AUTOCOM60220.2024.10486107
Witri, Nur, Anisa., Enik, Nurlaili, Afifah., Rudi, Hari, Murti. (2022). Selection of tomato breeding lines based on morphological traits associated with high yield potential in double- cross population. Biodiversitas, doi: 10.13057/biodiv/d230624 DOI: https://doi.org/10.13057/biodiv/d230624
Toshtemir, Ostonakulov., Gulshoda, Anvar, qizi, Saidova., Anvar, Akbarovich, Shamsiev. (2023). Isolation of adaptive varieties of tomato and innovative technologies for their cultivation on slightly saline soils of the Bukhara region. E3S web of conferences, doi: 10.1051/e3sconf/202338903012 DOI: https://doi.org/10.1051/e3sconf/202338903012
S., Vijayalakshmi., Aadhila, Begum, A, -., K., Preethi. (2024). Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network. International Journal For Multidisciplinary Research, doi: 10.36948/ijfmr.2024.v06i01.14350 DOI: https://doi.org/10.36948/ijfmr.2024.v06i01.14350
Murali, Krishna, Gumma., Ramavenkata, Mahesh, Nukala., Pranay, Panjala., Pavan, Bellam., Snigdha, Gajjala., S., K., Dubey., Vinay, Kumar, Sehgal., Ismail, Mohammed., Kumara, Charyulu, Deevi. (2024). Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms. AgriEngineering, doi: 10.3390/agriengineering6010045. DOI: https://doi.org/10.3390/agriengineering6010045
Jie, Zhang., Qingqing, Zhao., Xiaohong, Li., Hecang, Zang., Guoqiang, Li., Gu, Zheng. (2022). Design and Application of Crop Variety Recommendation and Production Decision System in Agricultural Production. doi: 10.1109/iccsmt58129.2022.00012. DOI: https://doi.org/10.1109/ICCSMT58129.2022.00012
Shivanoori, Sai, Samhith., T., V., Rajinikanth., Burma, Kavya., Alley, Yashwanth, Sai, Krishna. (2024). Crop Recommender System. doi: 10.1007/978-981-99-7954-7_55. DOI: https://doi.org/10.33564/IJEAST.2023.v07i10.015
Yogesh, Gupta., Geetika, Srivastava. (2024). Crop recommendation system for growing the best suitable crop. International Journal of Science and Research Archive, doi: 10.30574/ijsra.2024.12.1.1111. DOI: https://doi.org/10.30574/ijsra.2024.12.1.1111
D., Kavitha. (2023). Crop Recommendation System using ML. doi: 10.55041/isjem00457 DOI: https://doi.org/10.55041/ISJEM00457
Moritz, Marbach. (2021). Choosing Imputation Models. arXiv: Methodology DOI: https://doi.org/10.1017/pan.2021.39
Elenita, T., Caparino., Ariel, M., Sison., Ruji, P., Medina. (2018). A Modified Imputation Method to Missing Data as a Preprocessing Technique. doi: 10.1109/HNICEM.2018.8666394 DOI: https://doi.org/10.1109/HNICEM.2018.8666394
Suhas, Kakade., Ravi, S., Kulkarni., Somesh, Dhawale., M, J.C.. (2023). Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection. Green Intelligent Systems and Applications, doi: 10.53623/gisa.v3i2.313 DOI: https://doi.org/10.53623/gisa.v3i2.313
Marwa, S., Moustafa., Amira, S., Mahmoud., Eslam, Farg., Mohsen, Nabil., S.M., Yasir, Arafat. (2024). Bi-stage feature selection for crop mapping using grey wolf metaheuristic optimization. Advances in Space Research, doi: 10.1016/j.asr.2024.02.037 DOI: https://doi.org/10.1016/j.asr.2024.02.037
Zhanqing, Zhao., Guofei, Shang., Yubo, Liu., Shizhuo, Liu., Xiao, Zhang. (2023). Optimal feature selection and crop extraction using random forest based on GF-6 WFV data. International Journal of Remote Sensing, doi: 10.1080/01431161.2023.2216856. DOI: https://doi.org/10.1080/01431161.2023.2216856
Jiang, Y., et al. (2021). Evaluating Machine Learning Models for Predicting Building Energy Consumption: A Comparative Study. Energy and Buildings, 232, 110677. doi:10.1016/j.enbuild.2020.110677.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development, 7(3), 1247-1250. DOI: https://doi.org/10.5194/gmd-7-1247-2014
Kumar, A., & Singh, S. K. (2020). An Integrated Framework of Improved Mean Absolute Error Model for Image Denoising. Multimedia Tools and Applications, 79, 9935-9952. doi:10.1007/s11042-019-08354-5.
Najafabadi, A. H. Z., & Moosavi, V. (2018). Spatial modeling of wetland vegetation using remote sensing data: A comparison of four modeling approaches in the Anzali Wetland. Journal of Environmental Management, 206, 286-300. doi:10.1016/j.jenvman.2017.10.056. DOI: https://doi.org/10.1016/j.jenvman.2017.10.056
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Copyright (c) 2024 R. Usha Devi, Dr. N. A. Sheela Selvakumari
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