Soil Surface Texture Classification Using RGB Images Acquired Under Uncontrolled Field Conditions

Soil surface texture classification is a critical aspect of agriculture and soil science that affects various soil properties, such as water-holding capacity and soil nutrient retention. However, existing methods for soil texture classification rely on soil images taken under controlled conditions, which are not scalable for high spatiotemporal mapping of soil texture and fail to reflect real-world challenges and variations. To overcome these limitations, we propose a novel, scalable, and high spatial resolution soil surface texture classification process that employs image processing, texture-enhancing filters, and Convolutional Neural Network (CNN) to classify soil images captured under Uncontrolled Field Conditions (UFC). The proposed process involves a series of steps for improving soil image analysis. Initially, image segmentation is utilized to eliminate non-soil pixels and prepare the images for further processing. Next, the segmented output is divided into smaller tiles to isolate relevant soil pixels. Then, high-frequency filtering is introduced to enhance the texture of the images. Our research has shown that the Gabor filter is more effective than Local Binary Patterns (LBP) for this purpose. By creating four distinct Gabor filters, we can enhance specific, hidden patterns within the soil images. Finally, the split and enhanced images are used to train CNN classifiers for optimal analysis. We evaluate the performance of the proposed framework using different metrics and compare it to existing state-of-the-art soil texture classification frameworks. Our proposed soil texture classification process improves performance. We employed various CNN architectures in our proposed process for comparison purposes. Inception v3 produces the highest accuracy of 85.621%, an increase of 12% compared to other frameworks. With applications in precision agriculture, soil management, and environmental monitoring, the proposed novel methodology has the potential to offer a dependable and sustainable tool for classifying soil surface texture using low-cost ground imagery acquired under UFC.