Dandelion center detection in perennial ryegrass with Heat maps using Convolutional Neural Networks

Abstract

Industrial agricultural practices largely involve automated weed control processes and techniques. This phenomenon can also be seen in horticulture and landscaping. Generally, dandelion weeds (Taraxacum officinale) are a common pest and their detection is necessary for any type of removal. To address this detection issue, a cost-effective and intuitive labeling method using Heat maps is proposed for marking dandelion plant centers within perennial rye-grass. This method relies on approximate localization as opposed to pinpoint accuracy. An expandable lightweight Convolutional Neural Network (CNN) is built on a base network to generate detection output maps at two resolutions. Multiple loss functions are expanded to multi-instance predictions and their combinations are examined through ablation to assess and rank their performance. Different methods of computing standard performance metrics are also explored. Also, different backbone networks are also shown to reveal varying performance advantages. Through these methods, dandelion weed centers can consistently be located with robustness to noise and erroneous labels and with good precision. Furthermore, our method is almost entirely end-to-end. The experimental results demonstrate that our methods outperform Semantic Segmentation models in the precision of output maps while avoiding the need of intensive labeling costs. In addition, when applying Hierarchical Clustering to the segmentation maps for a complete comparison in center detection, our methods double the accuracy and do not require the manual tuning of cluster parameters. Our proposed application of soft computing can be used in the landscaping industry and adapted to other fields with relative ease. The binary classification and object detection tasks of locating dandelion plants can be extended to multi-class problems with other plants.