Deep-Learning Framework for Optimal Selection of Soil Sampling Sites

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.Comment: This paper is the full version of a poster presented at the AI in Agriculture Conference 2023 in Orlando, FL, US

Merging Field Work with Machine Learning : Exploring Andic Soil Development in the Cascade Range

Andic soils (aka Andisols) have unique properties that are important to society. For soil scientists, the genesis and taxonomy of Andisols is often confused because they can form in both volcanic and non-volcanic material. This dissertation seeks to address such confusion by looking at andic soil development in the Cascade Range through a series of complementary studies. The first study examines the influence of climate on soilscape diversity and soil development where andic properties exist or do not exist. The Cascade Range contains dually defined elevational and latitudinal threshold beyond which cool, moist conditions favor the formation of andic soil properties. The second study examines the effects of climate and parent material on the formation of andic soils. Precipitation, particularly in the form of snow, is crucial for the formation of andic soils, which challenges the status quo view that parent material is the dominant soil-forming factor of this class of soils. The third study examines the ability to predict and extrapolate andic and non-andic soils types with machine learning algorithms. From these three studies a better understanding of the amount, distribution, and processes of andic soil properties is gained. Using this knowledge, predicted climate change threatens over 140,000 hectares of Andisols and their ecological dependents in the Western Central Oregon Cascade Range over the next century

Jumble judging: Cognitive and affective outcomes of intercollegiate collaboration at a soil judging competition

Student-student interactions are influential parts of field experiences. While competitive judging events are a fun way to engage students in field-based learning, the focus on competition leads to an atmosphere that discourages collaboration between students. The objective of this study was to evaluate the cognitive and affective learning outcomes resulting from intercollegiate collaboration at a soil judging competition. Teams with students from two to three different universities were assigned and referred to as jumble judging teams. Jumble judging was held for the first time in the 2021 Region 5 Collegiate Soil Judging Contest. Learning outcomes were assessed using a pre- and post-survey, as well as group and individual reflections completed in the field. Student responses were generally positive, with 70% of students expressing agreement or strong agreement that they would like jumble judging to be included in future contests, 54% citing jumble judging as one of the best parts of the contest, and 93% identifying learning outcomes or describing an affective learning experience resulting from jumble judging. Evidence of both cognitive and affective learning were identified through student surveys and reflections. Overall, the event created a collaborative and collegial atmosphere and increased interaction between students from different universities, while maintaining the competitive nature of the event that motivates many students to get involved with judging teams.This is a manuscript of an article published as Young, Rebecca A., Judith K. Turk, Nicolas A. Jelinski, Amber D. Anderson, Kerry M. Clark, Ashlee Dere, Colby J. Moorberg, Kristopher Osterloh, and DeAnn Presley. "Jumble judging: Cognitive and affective outcomes of intercollegiate collaboration at a soil judging competition." Natural Sciences Education: e20104. doi:10.1002/nse2.20104. Posted with permission

Jumble judging: Cognitive and affective outcomes of intercollegiate collaboration at a soil judging competition

Student–student interactions are influential parts of field experiences. While competitive judging events are a fun way to engage students in field-based learning, the focus on competition leads to an atmosphere that discourages collaboration between students. The objective of this study was to evaluate the cognitive and affective learning outcomes resulting from intercollegiate collaboration at a soil judging competition. Teams with students from two to three different universities were assigned and referred to as jumble judging teams. Jumble judging was held for the first time in the 2021 Region 5 Collegiate Soil Judging Contest. Learning outcomeswere assessed using a pre- and postsurvey, as well as group and individual reflections completed in the field. Student responses were generally positive, with 70% of students expressing agreement or strong agreement that they would like jumble judging to be included in future contests, 54% citing jumble judging as one of the best parts of the contest, and 93% identifying learning outcomes or describing an affective learning experience resulting from jumble judging. Evidence of both cognitive and affective learning were identified through student surveys and reflections. Overall, the event created a collaborative and collegial atmosphere and increased interaction between students from different universities, while maintaining the competitive nature of the event that motivates many students to get involved with judging teams

Evaluating student attitudes and learning at remote collegiate soil judging events

As with many aspects of teaching, the COVID-19 pandemic forced soil judging teams to attempt new strategies towards achieving student learning outcomes. Soil judging Regions IV and V hosted remote regional contests in October 2020 in place of traditional, in-person contests typically held each fall. We conducted pre- and post-contest surveys to assess student learning outcomes, attitudes, and reflections on the remote contest experience compared to past, in-person contest experiences. We received 108 total responses from students who participated in the Region IV and Region V remote soil judging contests (>80% response rate). In self-reported learning outcomes, there were no significant gains post-contest and there were minimal differences between students in Regions IV and V. Female students, students with more soil judging experience, and students who had taken more soil science courses agreed more strongly that soil science is important, that they planned to pursue careers in soil science, and that they gained important skills from soil judging. Finally, students who previously participated in contests reported that they gained more knowledge and enjoyed in-person contests more than the remote contests held in Fall 2020. Thus, while it is possible to replicate some aspects of the soil judging experience in a remote contest, other aspects that are critical to student engagement are lost when teams are unable to gather at the contest location and examine soils in the field.This article is published as Owen, Rachel K., Amber Anderson, Ammar Bhandari, Kerry Clark, Morgan Davis, Ashlee Dere, Nic Jelinski et al. "Evaluating student attitudes and learning at remote collegiate soil judging events." Natural Sciences Education 50, no. 2 (2021): e20065. doi:10.1002/nse2.20065. Posted with permission.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made