Estimation of farmland soil roughness based on digital photo processing technology

Lu Xu; Yufei Zhou; Wanting Wang; Wanru Chen; Yike Niu; Xinyi Xu; Xiaoying Shi

doi:10.1117/12.2657123

18 October 2022 Estimation of farmland soil roughness based on digital photo processing technology

Lu Xu, Yufei Zhou, Wanting Wang, Wanru Chen, Yike Niu, Xinyi Xu, Xiaoying Shi

Proceedings Volume 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022); 123490W (2022) https://doi.org/10.1117/12.2657123
Event: International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 2022, Zhengzhou, China

Abstract

Soil roughness is important for characterizing soil surface, and plays an important role in many land surface models. Previous studies have proposed many methods to describe and model soil roughness. In this study, we use a simple way to measure soil surface roughness and try to find a simpler method to estimate it. We measure soil roughness in a ploughed farmland with an inelastic soft rope as an invention patent says, and then capture the photographs corresponding to the measured field points. Our philosophy is that soil roughness causes the shadows in the photo, so the shadows can be used to invert soil roughness. First, we binarize digital images to calculate the shadow area ratio in each photo. Different shadow area ratios will be obtained with different binarization threshold values, and here we use 6 threshold values to get 6 shadow area ratios. Then we try to use the 6 variables to model soil roughness with random forest method. We divide the dataset to two categories: training data (70%) for modeling and testing data (30%) for validation. We repeat the modeling process for 100 times to get the best model. The model shows a relatively satisfied result with a R2 of 0.81, and a RMSE of 0.05. We conclude that it is a viable way to estimate soil roughness with digital photos. The findings provide a simple and fast means for maximize the potential of digital cameras in estimating soil attributes in the field scales.

Citation Download Citation

Lu Xu, Yufei Zhou, Wanting Wang, Wanru Chen, Yike Niu, Xinyi Xu, and Xiaoying Shi "Estimation of farmland soil roughness based on digital photo processing technology", Proc. SPIE 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 123490W (18 October 2022); https://doi.org/10.1117/12.2657123

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