Research Article
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Estimating Corn Yield Using Statistical, Machine Learning and Deep Learning Methods

Year 2023, Volume: 40 Issue: 2, 74 - 80, 17.09.2023
https://doi.org/10.55507/gopzfd.1320542

Abstract

Yield estimation is an important field of study in agriculture. Forecasting yields provides producers, consumers, traders and policymakers with important preliminary information and time to take necessary action. Corn is an important product in terms of international trade and is widely used in human and animal nutrition throughout the world. Adana produces the highest amount of corn sown both as main and secondary product in Türkiye. Therefore, in this study, corn yield was tried to be estimated by using various meteorological parameters and plant fertilizer usage amounts. For this purpose, statistical (Auto-ARIMA), machine learning (Random Forest) and deep learning (CNN, LSTM) methods were used. The study findings showed that all models used predicted maize yield highly accurately. However, the highest accuracy LSTM model estimated the yield of first corn crop.

References

  • Biau, G., & Scornet, E. A. (2016). Random forest guided tour. TEST 25, 197–227 doi.org/10.1007/s11749-016-0481-7
  • FAOSTAT, (2023). Food and Agriculture Organization of the United Nations, Crops and Livestock Products Statistics, https://www.fao.org/faostat/en/#data/QCL
  • Fathima, M., Sowmya K., Barker, S., & Kulkarni, S. (2020). Analysis of Crop Yield Prediction using Data Mining Technique. International Research Journal of Engineering and Technology. 07(5) 10.13140/RG.2.2.14424.52482.
  • Hochreiter, S. & Schmidhuber, J. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.1997.9.8.1735.
  • Joshi, A., Biswajeet P., Shilpa, G, & Subrata C. (2023). Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. Remote Sensing. 15(8). 10.3390/rs15082014
  • Kim, P. (2017). Convolutional Neural Network. In: MATLAB Deep Learning. Apress, Berkeley, CA. doi.org/10.1007/978-1-4842-2845-6_6Kundu, S., Ghosh, A., Kundu, A. & Girish P. (2022) A ML-AI Enabled Ensemble Model for Predicting Agricultural Yield, Cogent Food & Agriculture, 8:1,  10.1080/23311932.2022.2085717
  • Matsuura, K., Gaitan, C., Hsieh, W., & Cannon, A. (2014). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science. 10.1017/S0021859614000392.
  • Paudel, D., Boogaard, H., Wit, A., Velde, M., Claverie, M., Nisini, L. Janssen, S., Osinga, S., & Athanasiadis, I. (2022). Machine learning for regional crop yield forecasting in Europe, Field Crops Research, 276, 10.1016/j.fcr.2021.108377.
  • Sharifi, A. (2020). Yield prediction with machine learning algorithms and satellite images. Journal of the Science of Food and Agriculture. 101. 10.1002/jsfa.10696.
  • TSI, (2023). Turkish Statistical Institute, Crop Production Statistics,https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=2
  • Yermal, L. & Balasubramanian, P. (2017). Application of Auto ARIMA Model for Forecasting Returns on Minute Wise Amalgamated Data in NSE, 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICCIC.2017.8524232.

İstatistiksel, Makine Öğrenmesi ve Derin Öğrenme Yöntemleri ile Mısır Verimi Tahmin

Year 2023, Volume: 40 Issue: 2, 74 - 80, 17.09.2023
https://doi.org/10.55507/gopzfd.1320542

Abstract

Tarımda verim tahmini önemli bir çalışma alanıdır. Verimin önceden tahmin edilmesi üreticilere, tüketicilere, tüccarlara ve politika yapıcılara önemli ön bilgiler sunmakta ve gerekli tedbirlerin alınması için zaman sağlamaktadır. Mısır, dünya genelinde insan ve hayvan beslenmesinde yaygın olarak kullanılan, uluslararası ticaret açıdan da önemli bir üründür. Adana ülkemizde mısır üretiminin hem ana ürün olarak hem de ikincil ürün olarak en yüksek miktarda yetiştirilen ildir. Bu nedenle, bu çalışmada, çeşitli meteorolojik parametreler ve bitki gübre kullanım miktarları kullanılarak mısır verimi tahmin edilmeye çalışılmıştır. Bu amaç doğrultusunda, istatistiksel (Auto-ARIMA), makine öğrenmesi (Random Forest) ve derin öğrenme (CNN, LSTM) yöntemleri kullanılmıştır. Çalışma bulguları, kullanılan tüm modellerin mısır verimini yüksek oranda doğru tahmin ettiğini göstermiştir. Bununla birlikte en yüksek doğruluk LSTM modeli ile birinci mısır ürünü verimini tahminde bulunmuştur.

References

  • Biau, G., & Scornet, E. A. (2016). Random forest guided tour. TEST 25, 197–227 doi.org/10.1007/s11749-016-0481-7
  • FAOSTAT, (2023). Food and Agriculture Organization of the United Nations, Crops and Livestock Products Statistics, https://www.fao.org/faostat/en/#data/QCL
  • Fathima, M., Sowmya K., Barker, S., & Kulkarni, S. (2020). Analysis of Crop Yield Prediction using Data Mining Technique. International Research Journal of Engineering and Technology. 07(5) 10.13140/RG.2.2.14424.52482.
  • Hochreiter, S. & Schmidhuber, J. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.1997.9.8.1735.
  • Joshi, A., Biswajeet P., Shilpa, G, & Subrata C. (2023). Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. Remote Sensing. 15(8). 10.3390/rs15082014
  • Kim, P. (2017). Convolutional Neural Network. In: MATLAB Deep Learning. Apress, Berkeley, CA. doi.org/10.1007/978-1-4842-2845-6_6Kundu, S., Ghosh, A., Kundu, A. & Girish P. (2022) A ML-AI Enabled Ensemble Model for Predicting Agricultural Yield, Cogent Food & Agriculture, 8:1,  10.1080/23311932.2022.2085717
  • Matsuura, K., Gaitan, C., Hsieh, W., & Cannon, A. (2014). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science. 10.1017/S0021859614000392.
  • Paudel, D., Boogaard, H., Wit, A., Velde, M., Claverie, M., Nisini, L. Janssen, S., Osinga, S., & Athanasiadis, I. (2022). Machine learning for regional crop yield forecasting in Europe, Field Crops Research, 276, 10.1016/j.fcr.2021.108377.
  • Sharifi, A. (2020). Yield prediction with machine learning algorithms and satellite images. Journal of the Science of Food and Agriculture. 101. 10.1002/jsfa.10696.
  • TSI, (2023). Turkish Statistical Institute, Crop Production Statistics,https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=2
  • Yermal, L. & Balasubramanian, P. (2017). Application of Auto ARIMA Model for Forecasting Returns on Minute Wise Amalgamated Data in NSE, 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICCIC.2017.8524232.
There are 11 citations in total.

Details

Primary Language English
Subjects Biosystem
Journal Section Research Articles
Authors

Cevher Özden 0000-0002-8445-4629

Publication Date September 17, 2023
Published in Issue Year 2023 Volume: 40 Issue: 2

Cite

APA Özden, C. (2023). Estimating Corn Yield Using Statistical, Machine Learning and Deep Learning Methods. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 40(2), 74-80. https://doi.org/10.55507/gopzfd.1320542