Deep Learning-Based Soil Texture Segmentation with Yolov8 Model

The analysis of traditional soil texture by chemical methods is both time-consuming and subject ‎to environmental hazards at a high cost. The researcher outlines a new Deep YOLOv8 ‎Segmentation approach (DYV8S), which segments soil textures in Un-Inhibited Arena ‎Circumstances (UAC) pictures. The research focuses on improving the detection of soil textures ‎through external factor reduction and avoids the requirement for chemical analysis. The proposed ‎DYV8S technique involves execution through four separate processes. High-resolution UAC ‎field images undergo a USDA-trio and YOLOv8 combination for extracting soil pixels. Semantic ‎segmentation of non-soil pixels occurs through the application of Tierce-SegNet, UNet, and ‎DeepLabV3+ models following their deployment for pixel removal. The implementation of ‎manifold Gabor filters extracts enhanced texture information associated with soil classification ‎improvement. A deep YOLOv8 model operating with adjusted hyperparameters performs pre-‎defined categories of soil texture classification and stitching procedures. The training involved ‎soil samples belonging to seven different texture classes. The DYV8S model delivered a 99.8% ‎accuracy rate at the same time, it showed a 0.05 error rate throughout the soil texture identification ‎process. Scientists documented three performance metrics, namely 0.869 Jaccard index and 0.931 ‎Dice Similarity Coefficient, and 0.976 Kappa statistic. The algorithm analysis established that ‎DYV8S achieved maximum accuracy in addition to exhibiting low misclassification rates. The ‎experimental method demonstrated high time efficiency while maintaining low expense levels ‎and safety aspects when compared to conventional chemical techniques. Maintaining controlled ‎environmental conditions alongside controlled illumination decreased the amount of distortion ‎during soil image analysis. High accuracy happens when the DYV8S model successfully ‎categorizes soil texture which beats traditional classification approaches. The integrated ‎processing strategy of segmentation with feature enhancement and deep learning systems ‎delivers a dependable method for soil texture classification while remaining quick and ‎economical, which improves decision-making for crops and fertilizers‎.