A Deep Learning Based Bio Fertilizer Recommendation Model Based on Chlorophyll Content for Paddy Leaves
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50993-3_25
Reference11 articles.
1. Khoshrou, M.I., Zarafshan, P., Dehghani, M., Chegini, G., Arabhosseini, A., Zakeri, B.: Deep learning prediction of chlorophyll content in tomato leaves. In: 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, Republic of Islamic, pp. 580–585 (2021). https://doi.org/10.1109/ICRoM54204.2021.9663468
2. Paul, S., Poliyapram, V., İmamoğlu, N., Uto, K., Nakamura, R., Kumar, D.N.: Canopy averaged chlorophyll content prediction of pear trees using convolutional auto-encoder on hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 1426–1437 (2020). https://doi.org/10.1109/JSTARS.2020.2983000
3. Gao, J., Zhang, Z.: Study on deep learning model for online estimation of chlorophyll content based on near ground multispectral feature bands. IEEE Access 10, 132183–132192 (2022). https://doi.org/10.1109/ACCESS.2022.3230355
4. El-Hendawy, S., et al.: Combining hyperspectral reflectance indices and multivariate analysis to estimate different units of chlorophyll content of spring wheat under salinity conditions. Plants 11(3), 456 (2022). https://doi.org/10.3390/plants11030456
5. Youseftabar, S., Sharifabad, H.H., Heravan, I.M.: Rice cropping system in semi-mechanized and traditional planting patterns in Northern Iran. J. Plant Nutr. 44(7), 970–987 (2021). https://doi.org/10.1080/01904167.2020.1845380
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