Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 22; p. 5846
• Main Authors: Pan, Yuyun, Zhu, Nengzhi, Ding, Lu, Li, Xiuhua, Goh, Hui-Hwang, Han, Chao, Zhang, Muqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Accuracy; Aerial photography; Algorithms; Artificial intelligence; convolutional neural network; Corn; Cotton; Decision making; Deep learning; faster RCNN; Feature extraction; Machine learning; Morphology; object detection; Precipitation; Precision farming; Seedlings; Software; Sugarcane; sugarcane seedling; Support vector machines; unmanned aerial vehicle; Unmanned aerial vehicles; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14225846

Sugarcane seedling emergence is important for sugar production. Manual counting is time-consuming and hardly practicable for large-scale field planting. Unmanned aerial vehicles (UAVs) with fast acquisition speed and wide coverage are becoming increasingly popular in precision agriculture. We provide a method based on improved Faster RCNN for automatically detecting and counting sugarcane seedlings using aerial photography. The Sugarcane-Detector (SGN-D) uses ResNet 50 for feature extraction to produce high-resolution feature expressions and provides an attention method (SN-block) to focus the network on learning seedling feature channels. FPN aggregates multi-level features to tackle multi-scale problems, while optimizing anchor boxes for sugarcane size and quantity. To evaluate the efficacy and viability of the proposed technology, 238 images of sugarcane seedlings were taken from the air with an unmanned aerial vehicle. Outcoming with an average accuracy of 93.67%, our proposed method outperforms other commonly used detection models, including the original Faster R-CNN, SSD, and YOLO. In order to eliminate the error caused by repeated counting, we further propose a seedlings de-duplication algorithm. The highest counting accuracy reached 96.83%, whilst the mean absolute error (MAE) reached 4.6 when intersection of union (IoU) was 0.15. In addition, a software system was developed for the automatic identification and counting of cane seedlings. This work can provide accurate seedling data, thus can support farmers making proper cultivation management decision.