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Survey on Progress in Agricultural Yield Prediction-from Machine Learning to Deep Learning

Neetu Agarwal, Susmita Ray, K. C. Tripathi


Machine learning has offered new prospects for data-intensive study in the multidisciplinary field of Agri-technology, especially with the rise of big data systems and advanced computers. Machine learning is a powerful decision-making tool for predicting crop yields, as well as determining which crops to plant and what activities to engage in during the crop's growing season. To facilitate the study of the prediction of the agricultural yield many techniques of machine learning have been used. We have seen a lot of progress in agriculture during the previous few decades. During this time, there was a significant shift from simpler algorithms of machine learning to the application of algorithms of deep learning. This study extracts and synthesises the techniques and features which focused on the prediction of crop yield research, specifically connected to machine learning, neural networks, and deep learning, from papers published in last decade. This study examines the work of several researchers to provide a quick summary of the present state of automation in agriculture. In today’s scenario the systems related to Farm management are growing into real-time artificial intelligence-enabled programmes that provides the awareness for taking decisions and actions to be taken by farmer by applying machine learning.

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Ramos, P.J., Prieto, F.A., Montoya, E.C. and Oliveros, C.E., 2017. Automatic fruit count on coffee branches using computer vision. Computers and Electronics in Agriculture, 137, pp. 9–22.

Amatya, S., Karkee, M., Gongal, A., Zhang, Q. and Whiting, M.D., 2016. Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting. Biosystems engineering, 146, pp. 3–15.

Sengupta, S. and Lee, W.S., 2014. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Engineering, 117, pp. 51–61.

Ali, I., Cawkwell, F., Dwyer, E. and Green, S., 2016. Modeling managed grassland biomass estimation by using multitemporal remote sensing data—A machine learning approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (7), pp. 3254–3264.

Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L. and Mouazen, A.M., 2016. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and electronics in agriculture, 121, pp. 57–65.

J. Senthilnath, A. Dokania, M. Kandukuri, R.K.N., G. Anand, and S.N. Omkar, “Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV,” Biosyst. Eng., vol. 146, pp. 16–32, 2016, doi: 10.1016/j.biosystemseng.2015.12.003.

Su, Y.X., Xu, H. and Yan, L.J., 2017. Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi journal of biological sciences, 24 (3), pp. 537–547.

Kung, H.Y., Kuo, T.H., Chen, C.H. and Tsai, P.Y., 2016. Accuracy analysis mechanism for agriculture data using the ensemble neural network method. Sustainability, 8 (8), p.735.

Shahhosseini, M., Hu, G. and Archontoulis, S.V., 2020. Forecasting corn yield with machine learning ensembles. Frontiers in Plant Science, 11, p.1120.

Schierhorn, F., Hofmann, M., Adrian, I., Bobojonov, I. and Müller, D., 2020. Spatially varying impacts of climate change on wheat and barley yields in Kazakhstan. Journal of Arid Environments, 178, p.104164.

Kaul, M., Hill, R.L. and Walthall, C., 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85 (1), pp. 1–18.

Ye, X., Sakai, K., Garciano, L.O., Asada, S.I. and Sasao, A., 2006. Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological modelling, 198 (3–4), pp. 426–432.

Khaki, S., Pham, H. and Wang, L., 2021. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Scientific Reports, 11 (1), pp. 1–14.

R. Mythili, “a New Crop Yield Prediction System Using Random Forest Combined With Least Squares Support Vector Machine,” J. Mech. Contin. Math. Sci., vol. 15, no. 5, pp. 90–100, 2020, doi: 10.26782/jmcms.2020.05.00008.

Wolanin, A., Mateo-García, G., Camps-Valls, G., Gómez-Chova, L., Meroni, M., Duveiller, G., Liangzhi, Y. and Guanter, L., 2020. Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt. Environmental Research Letters, 15 (2), p.024019.

P. Charoen-Ung and P. Mittrapiyanuruk, “Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning,” Adv. Intell. Syst. Comput., vol. 769, no. January, pp. 33–42, 2019, doi: 10.1007/978-3-319-93692-5_4.

Ahmad, I., Saeed, U., Fahad, M., Ullah, A., Habib ur Rahman, M., Ahmad, A. and Judge, J., 2018. Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46 (10), pp. 1701–1711.

P. Taherei Ghazvinei et al., “Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network,” Eng. Appl. Comput. Fluid Mech., vol. 12, no. 1, pp. 738–749, 2018, doi: 10.1080/19942060.2018.1526119.

Villanueva, B.M. and Salenga, M.L.M., 2018. Bitter melon crop yield prediction using machine learning algorithm. Int. J. Adv. Comput. Sci. Appl, 9, pp. 1–6.

Li, B., Xu, X., Zhang, L., Han, J., Bian, C., Li, G., Liu, J. and Jin, L., 2020. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, pp. 161–172.

Prasad, N.R., Patel, N.R. and Danodia, A., 2021. Crop yield prediction in cotton for regional level using random forest approach. Spatial Information Research, 29 (2), pp. 195–206.

Monga, T., 2018, May. Estimating vineyard grape yield from images. In Canadian Conference on Artificial Intelligence (pp. 339–343). Springer, Cham.

Nassar, L., Okwuchi, I.E., Saad, M., Karray, F., Ponnambalam, K. and Agrawal, P., 2020, July. Prediction of strawberry yield and farm price utilizing deep learning. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE.

Wei, M.C.F., Maldaner, L.F., Ottoni, P.M.N. and Molin, J.P., 2020. Carrot yield mapping: A precision agriculture approach based on machine learning. AI, 1 (2), pp. 229–241.

Huntington, T., Cui, X., Mishra, U. and Scown, C.D., 2020. Machine learning to predict biomass sorghum yields under future climate scenarios. Biofuels, Bioproducts and Biorefining, 14 (3), pp. 566–577.


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