Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

Di Zhang, Feng Pan, Qi Diao, Xiaoxue Feng, Weixing Li and Jiacheng Wang
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Di Zhang: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Feng Pan: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Qi Diao: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Xiaoxue Feng: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Weixing Li: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Jiacheng Wang: School of Automation, Beijing Institute of Technology, Beijing 100081, China

Agriculture, 2021, vol. 12, issue 1, 1-18

Abstract: With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

Keywords: chili detection; prototypical network; small-scale similarity problem; unmanned aerial vehicle images (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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