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• Soils from three agro-climatic zones were classified using MSCC and Nix. • Soil OC was predicted using MSCC and Nix in different combinations. • Both non– standardized and MSCC-standardized Nix values were used. • Results indicated the reproducibility and potential accuracy of the Nix sensor. • Nix + MSCC produced satisfactory surface OC prediction. Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (Nix STD) and without MSCC standardization (Nix NON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the Nix STD , an acceptable mean color difference (ΔE* ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the Nix STD /Nix NON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using Nix NON-STD data alone. Besides, the combination of the MSCC and the Nix NON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ. [ABSTRACT FROM AUTHOR]

• A setup containing a smartphone and a dark chamber was used for predicting soil texture. • Soil images were acquired using the setup and analyzed via computer vision, RF, and CNN models. • Both RF and CNN showed high prediction accuracy for clay and sand, with moderate prediction accuracy for silt. • Image-extracted color features showed the maximum influence on the RF model performance. • An Android app using the calibrated CNN model was able to predicted soil textural values with satisfactory accuracy. The rapid and non-invasive prediction of soil sand, silt, and clay is becoming increasingly attractive given the laborious nature of traditional soil textural analysis. This study proposed a novel and cheap setup comprising a smartphone, a custom-made dark chamber, and a smartphone application for predicting soil texture of the dried, ground, and sieved samples. The image acquisition system was used to capture triplicate images from 90 mineral soil samples, representing a wide textural variability from sand to clay. Local features, color features, and texture features were extracted from the cropped images and subsequently used in different combinations to predict laboratory-measured clay, silt, and sand via random forest (RF) and convolutional neural network (CNN) algorithms. Results indicated high prediction accuracy for clay (R2 = 0.97–0.98) and sand (R2 = 0.96–0.98) and moderate prediction accuracy for silt (R2 = 0.62–0.75) using both algorithms. Color features outperformed all other image-extracted features and showed the maximum influence on RF model performance. The better performance of the color features can be attributed to the color features of mineral matter and soil organic matter (SOM). An Android-based smartphone application based on the calibrated CNN model was able to predict and return soil textural values. These results exhibited the potential of the proposed system as a proximal sensor for rapid, cost-effective, and eco-friendly soil textural analysis using computer-vision and deep learning. More research is warranted to augment the setup design, develop a standalone mobile application, and measure the impacts of soil moisture and high SOM on the model prediction performance to extend the approach for on-site prediction of soil texture. [ABSTRACT FROM AUTHOR]