Multimodal Sensing to Estimate Soil Organic Carbon Using Limited Samples from Paddy Fields

The analysis of soil carbon helps various sectors, including agriculture, in the context of monitoring soil health. In precision agriculture, decisions are made on the basis of site-specific information and thus have the potential to increase crop productivity more than is possible with traditional high-input agriculture. Site-specific information-based nutrition management, pest and disease management, and water management are the main areas of interest in the era of precision agriculture. Soil organic carbon (SOC) is one of the main components of the carbon cycle and impacts soil physical and chemical properties. Soil color is considered an indicator of soil carbon. In relation to soil physical properties, soil color has been used to determine SOC level and classification throughout history in a qualitative manner, and recently, researchers have shown interest in relating soil color data to quantify soil chemical properties. From spectroscopy-based color analysis to image-based color analysis, research has shown strong relationships between SOC and color properties. Therefore, with the improvement of technology to create smaller and portable sensors, the potential exists to automate the processes of soil chemical analysis to use them in precision agriculture. Two of the major limitations of these methodologies in research are the number of known soil samples required to calibrate a model (the majority of the models require more than 100 samples) and the use of expensive spectrometers with complex processes. Thus, the potential of individual farmers to deploy these methods is limited. This research was conducted to develop a methodology with complete guidelines and a set of tools to allow farmers to analyze SOC themselves. Furthermore, by encouraging farmers to analyze their farmland soils for SOC and update the data, the research enables them to potentially use this information to manage their agronomic practices, including the addition of organic fertilizer to reduce soil carbon pool inefficiencies and decisions regarding the mode of tillage and water management. During this research, three sensors and different combinations of sensors were used to capture soil surface color, temperature, and reflectance and were considered for model development. The highest-model-fit equation was obtained from the thermal image and red, green, and blue (RGB) image combinations (R2 = 0.65 and MSE = 0.0335). The variables used for X from the color models were hue values and redness (a), and those from the thermal image minimum and maximum temperature data were used. Finally, using a regression equation along with the image data and SOC data from the chemical analysis, a farmer-feedback-based SOC prediction model was developed.