Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only $9\%$ for the top 5 predictions, the accuracy of the proposed method is $74\%$, which is significant.

Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user’s perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below $40\%$, the page-level prediction is below $65\%$, and the accuracy with patching is around $95\%$ for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method’s potential scalability and effectiveness with larger datasets.

A quantitative understanding of actual evapotranspiration (ET_a) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ET_a rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers.