Growth monitoring of rapeseed seedlings in multiple growth stages based on low-altitude remote sensing and semantic segmentation

Abstract

Rapeseed seedling growth monitoring indicates growth status and detects problems, such as seedling gaps, seedbed unevenness, and diseases or insect pests in time, which play an important role in improving sowing strategies, promoting the decision-making of fertilizer prescription, and increasing economic efficiency. To improve the accuracy of rapeseed seedling growth assessment, a multi-growth stage growth assessment method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and semantic segmentation was proposed to assess the growth of rapeseed into excellent, average, and poor growth. First, to address the problem of complex field scenes and densely planted rapeseed leading to difficult segmentation of rapeseed seedlings and field drains, the original Deeplabv3+ model was improved by selecting the lightweight network MobileNetV2 as the backbone feature extraction network and fusing the coordinate attention（CA）module, which enables the model to better noise removal and feature extraction and improves the model’s accuracy and robustness. Then, a field drain optimal centerline algorithm is proposed to obtain the optimal centerline of all field drain in the image and determine the field box position. Finally, eight growth-related feature values for rapeseed seedling were constructed, and were used as feature vectors in a random forest (RF) to construct multi-growth stage growth assessment model for rapeseed seedlings. The results indicate that the improved DeeplabV3+ network outperformed the original DeeplabV3+ network, with the mean pixel accuracy increasing from 78.43 % to 87.47 % (an improvement of 9.04 %) and the average intersection over union (mIoU) increasing from 67.45 % to 76.89 % (an improvement of 9.44 %). The mean positional deviation of the centerline was –5.29 pixels with a standard deviation of 9.51 and a mean angular deviation of –0.01848 rad with a standard deviation of 0.00791, which can effectively detect the centerline of the field drain. The precision, sensitivity, specificity, and accuracy of the proposed method were 96.35 %, 96.34 %, 97.20 %, and 96.34 %, respectively. The algorithm of this study can efficiently segment rapeseed seedlings and field drains, obtain the optimal centerline of the field drain, and be used for rapeseed seedling multi-growth stage growth monitoring, which provides a theoretical basis and technical reference for rapeseed seedling multi-growth stage growth monitoring.