Peanut yield estimation models using Machine Learning techniques and Google Earth Engine

• Jarlyson Brunno Costa Souza UNESP
• Franklin Daniel Inácio UFLA
• Samira Luns Hatum de Almeida UNESP
• Armando Lopes de Brito Filho UNESP
• Adão Felipe dos Santos UFLA
• Rouverson Pereira da Silva UNESP

DOI: https://doi.org/10.5753/sbiagro.2023.26546

Resumo

The estimation of the yield for the peanut crop can be performed by visual methods, traditional destructive methods, which are time consuming, laborious and with imprecise predictions. Thus, the objective was to use SR techniques and Artificial Neural Networks (ANN) in the development of an innovative method for yield prediction in the peanut crop. The experiments were conducted in the state of São Paulo in the 2021/2022 crop season, in 6 commercial farms in sandy and clayey areas. The RBF and MLP networks were able to estimate peanut yield with an accuracy below 1000 kg/ha. GNDVI was a better vegetation index with estimation accuracy of 238.7 and 296.54 for the RBF and MLP networks, respectively.

Referências

Joshi, Vijaya R. et al. In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt. International Journal Of Biometeorology, [S.L.], v. 65, n. 4, p. 489-502, 21 nov. 2020. Springer Science and Business Media LLC. http://dx.doi.org/10.1007/s00484-020-02039-z.

Souza, J. B. C., de Almeida, S. L. H., Freire de Oliveira, M., Santos, A. F. D., Filho, A. L. D. B., Meneses, M. D., & Silva, R. P. D. (2022). Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks. Agronomy, 12(7), 1512.

Santos, A. F., Lacerda, L. N., Rossi, C., Moreno, L. D. A., Oliveira, M. F., Pilon, C., ... & Vellidis, G. (2022). Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks. Remote Sensing, 14(1), 93.

Sentinel2UserHandbook.Availableonline: [link]. (Acessado em 06/04/2022).

Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.

Haykin, S., & Principe, J. (1998). Making sense of a complex world [chaotic events modeling]. IEEE Signal Processing Magazine, 15(3), 66-81.

Yoosefzadeh-Najafabadi, M., Earl, H. J., Tulpan, D., Sulik, J., & Eskandari, M. (2021). Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean. Frontiers in plant science, 11, 624273.

Zhang, J., Tian, H., Wang, P., Tansey, K., Zhang, S., & Li, H. (2022). Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China. Computers and Electronics in Agriculture, 192, 106616.

Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., & Luck, B. (2020). Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sensing, 12(12), 2028.

Rocha, A. D., Groen, T. A., Skidmore, A. K., Darvishzadeh, R., & Willemen, L. (2018). Machine learning using hyperspectral data inaccurately predicts plant traits under spatial dependency. Remote sensing, 10(8), 1263.