Using machine learning to update soil survey

Soil survey in the recent past seems to be taking a paradigm shift with the advent of various geospatial and pedometric techniques. The impetus for this includes both the usability and the limitations associated with traditional soil survey products. This research explores the use of machine learning and GIS tools for updating an existing soil survey of Monroe County in Southeastern Ohio. A soil-landscape modeling framework was adopted to predict soil series based on a number of high-resolution geospatial environmental correlates. Base data layers included the existent soil survey, climate attribute surfaces (precipitation and evapotranspiration), historic vegetation, terrain attributes derived from digital elevation models, and bedrock geology. The old soil survey was randomly sampled to generate a pre-classified training set containing the target soil series and their environmental correlates. Two machine learning algorithms (J48 and Random Forests) were used to build classification models. The built models were then applied to the entire county to generate digital soil maps. The models predicted the correct dominant soil series about 60 percent of the time. When compared with the existent soil survey map, the digital soil map was able to predict even the components with in a soil mapunit. Machine learning can efficiently be used to get new insights into the traditional soil maps and can be used as a guide for further field investigations

Characterizing the Decision Process in Setting Corn and Soybean Seeding Rates

Selecting optimal corn and soybean seeding rates are difficult decisions to make. A survey of Ohio and Michigan farm operators finds that, although generally keen to learn from others, they tend to emphasize their own experience over outside information sources. Soybean growers declare university and extension recommendations as more important than do corn growers. In response to direct queries and in free comments, growers place more emphasis on understanding the agronomic and technological problems at hand than on adjusting to the market environment. Given the decision environment, we argue that these responses are reasonable

Correction: Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets.

[This corrects the article DOI: 10.1371/journal.pone.0213356.]

Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets.

This paper highlights the importance of optimized shape index for agricultural management system analysis that utilizes the contiguous bands of hyperspectral data to define the gradient of the spectral curve and improve image classification accuracy. Currently, a number of machine learning methods would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three new optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of optimized shape indices, specifically the Moment Distance Ratio Right/Left (MDRRL), to discriminate between types of tillage (corn-min and corn-notill) and between grass/pasture and grass/trees, tree and grass under object-based random forest approach

Perimeter-Area Soil Carbon Index (PASCI): modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment

ABSTRACTSoil Organic Carbon (SOC) is the most important indicator of soil health and determines long-term crop productivity. Here, we applied the Random Forest regression model to soil hyperspectral data to determine the important spectral bands and regions for SOC retrieval. Multiple existing studies already identified specific wavelength bands that could be good indicators of SOC. However, there is no hyperspectral-based method that is currently available to simultaneously investigate these identified specific wavelength regions for SOC. To help fill this gap, we developed the Perimeter-Area Soil Carbon Index (PASCI) that utilized optimal SOC spectral bands and then evaluated its robustness for SOC prediction and retrieval against other existing indices. The results of regression analysis between SOC and PASCI values showed a significant relationship (r2 = 0.76; p < 0.05). A significant statistical relationship (r2 = 0.73) was also observed between SOC and the sum indices. The results from this study have advanced our understanding of the optimal spectral bands for SOC. Finally, the PASCI could be applied to hyperspectral and multispectral images to remotely quantify, predict, and map SOC