外文摘要：Consistency of crop establishment is a measure of uniformity of crop attributes, such as plant stand count, crop emergence rate, and plant spacing across the field. Quantifying consistency during the early crop growth stage is important for establishment decisions to use targeted nutrients and to facilitate timely replanting in inconsistent crop regions. Crop consistency can be analysed using two key parameters: plant stand count and spacing statistics since they provide insight into plant density and its emergence percentage. However, manual assessment of them is time-consuming, prone to errors, and labour-intensive in large fields. An alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings. We use the YOLOv5-based object detection model for plant counting, which attains a mean average precision of 0.956 to detect Canola plants. A Lightweight U -Net model is proposed to segment rows, followed by Guo-Hall thinning and Probabilistic Hough Transform to determine inter -row and inter -plant spacing. Our proposed row segmentation model achieves a mean Intersection over Union (mIoU) of 0.8444 with class -wise IoU of 0.9925 and 0.6963 for background and crop using fewer parameters. The new architecture uses only 14M parameters and achieves performance comparable to the state-of-the-art U -Net (32.5M) and SegNet (29M).
外文关键词：Lightweight U-net;Crop consistency;Probabilistic hough transform;Guo-Hall thinning;Plant density estimation
作者：Ullah, Muhib;Islam, Fatimah;Bais, Abdul
作者单位：Univ Regina
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：217
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台