Automated detection of Crop-Row lines and measurement of maize width for boom spraying

Abstract

Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage.