Nodi Sadia Sabrin, Paul Manoranjan, Robinson Nathan, Wang Liang, Rehman Sabih Ur, Kabir Muhammad Ashad
School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.
Cooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, Australia.
Sensors (Basel). 2025 Jan 6;25(1):287. doi: 10.3390/s25010287.
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.
土壤颜色是土壤健康及相关特性的关键指标。在农业领域，土壤颜色为农民和顾问提供了直观指南，以解读土壤功能和性能。孟塞尔色卡多年来一直用于确定土壤颜色，但该过程存在误差，因为它取决于使用者的感知。由于智能手机广泛使用且配备高质量摄像头，本研究使用一款流行的智能手机来拍摄图像。本研究旨在通过对手机拍摄的图像运用深度学习技术，从孟塞尔土壤颜色手册（MSCB）预测孟塞尔土壤颜色（MSC）。MSCB包含14页和443个色卡。因此，逐卡预测的类别数量非常多，而拍摄的图像不足以使用深度学习方法进行训练和验证；于是，提出了一种基于图像块的机制来扩充数据集。所以，行动过程是通过评估多种深度学习方法并结合基于图像块的机制，来找出MSC在页面级别和色卡级别上的预测准确率。该分析还提供了关于MSC预测的最佳深度学习技术的知识。不采用图像块机制时，色卡级预测的准确率低于40%，页面级预测低于65%，而采用图像块机制时，两者的准确率均约为95%，这是相当可观的。最后，本研究深入探讨了所提出的技术和分析在实际土壤中的应用，并在土壤样本数量有限的情况下提供了更高准确率的结果，表明所提出的方法在处理更大数据集时具有潜在的可扩展性和有效性。
