The Use Remotely Piloted Aircraft in Counting Agricultural and Forestry Plants: a Systematic Review

Abstract

In recent years, plant counting using data collected by sensors embedded in remotely piloted aircraft systems (RPAS), combined with machine learning algorithms, has become popular in the agroforestry sector, especially in crop planning, crop production estimation, among other applications. This study aimed to perform a systematic review of the literature on plant counting in the agricultural and forestry sector that uses data from sensors embedded in RPAS. We sought to identify the principal bibliometric indicators of scientific production and, through content analysis, the main characteristics and trends of the studies. A total of 33 scientific articles obtained on the Scopus and Web of Science platforms were used. Then, a content qualitative analysis of each article was conducted to identify the main thematic categories: agricultural and forest species, platform and sensors, software, and algorithm. There was an increase in scientific publications as of 2017. The USA presented the higher number of researches performed, with eight publications. There was a significant presence of RGB (Red, Green and Blue) sensors followed by multispectral. The algorithms Convoluctional Neural Network (CNN), Structure from Motion (SfM), and K-means stood out for the recurrence of use, either singularly or associated. Studies with this purpose drive new research development, where this technology utilization is revealed as a potential instrument to understand the usage trends, subsidize and encourage the information acquisition, promoting improvements and progress for research in the agroforestry scope.

Plant phenotyping relevance

農林植物のセンサー画像と機械学習による植物個体数推定を主題とする系統的レビューであり、植物形質取得手法のレビューが中心である。

Code and data availability

This is a systematic review of 33 published articles; the paper reports no original phenotyping measurements, images, code, or models of its own. The data availability statement only points to the published literature, and no author-deposited dataset, code, or supplement is described.

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