Smartphone-based straw incorporation: An improved convolutional neural network

Abstract

In conservation tillage, the effective incorporation of straw into soil is a critical factor for optimizing decomposition rates and addressing challenges related to straw accumulation. Consequently, this study introduces an innovative straw semantic segmentation model to extract straw mixed with soil from images, and proposes a method to measure the quality of straw and soil mixing based on this model. We conducted field experiments based on this method, analyzed the quality of straw incorporation at different depths, and finally integrated the model into a mobile application to facilitate the automatic, accurate, and rapid assessment of the quality of straw and soil mixing. The methodology of this study as an improved convolutional neural network entailed the fusion of deep separable convolution with a multi-scale feature extraction module, utilizing convolution kernels of varying sizes to capture the diverse features of straw. Further refinement included halving the first convolution channel and implementing a direct connection structure. Attention gates were strategically deployed to enhance salient features, resulting in superior accuracy compared to the original U-shaped network, fully convolutional network, and Otsu algorithm. Notably, the proposed model achieved enhanced performance while reducing the number of parameters and model complexity to one-third and one-half of the original U-shaped network, respectively. For a quantitative description of the rate and uniformity of straw incorporation, this study employed image processing technology and a grid counting method. The synergistic integration of smartphone imagery and deep learning expedited the delivery of rapid and reliable results, promising practical applicability in future straw incorporation practices. A demonstration video of the application is accessible at: https://doi.org/10.6084/m9.figshare.14480745.v3. The findings of this research contribute significantly to the advancement of conservation tillage by establishing a robust framework for the assessment and enhancement of straw incorporation operations’ efficiency.