  This work leverages the recent advancements of deep learning in image
processing to find optimal locations that present the important characteristics
of a field. The data for training are collected at different fields in local
farms with five features: aspect, flow accumulation, slope, NDVI (normalized
difference vegetation index), and yield. The soil sampling dataset is
challenging because the ground truth is highly imbalanced binary images.
Therefore, we approached the problem with two methods, the first approach
involves utilizing a state-of-the-art model with the convolutional neural
network (CNN) backbone, while the second is to innovate a deep-learning design
grounded in the concepts of transformer and self-attention. Our framework is
constructed with an encoder-decoder architecture with the self-attention
mechanism as the backbone. In the encoder, the self-attention mechanism is the
key feature extractor, which produces feature maps. In the decoder, we
introduce atrous convolution networks to concatenate, fuse the extracted
features, and then export the optimal locations for soil sampling. Currently,
the model has achieved impressive results on the testing dataset, with a mean
accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean
Dice Coefficient of 71.47%, while the performance metrics of the
state-of-the-art CNN-based model are 66.08%, 3.85%, and 1.98%, respectively.
This indicates that our proposed model outperforms the CNN-based method on the
soil-sampling dataset. To the best of our knowledge, our work is the first to
provide a soil-sampling dataset with multiple attributes and leverage deep
learning techniques to enable the automatic selection of soil-sampling sites.
This work lays a foundation for novel applications of data science and
machine-learning technologies to solve other emerging agricultural problems.
