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Technical Note

GRID: A Python Package for Field Plot Phenotyping Using Aerial Images

Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
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Remote Sens. 2020, 12(11), 1697; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111697
Received: 27 April 2020 / Revised: 24 May 2020 / Accepted: 25 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Aerial imagery has the potential to advance high-throughput phenotyping for agricultural field experiments. This potential is currently limited by the difficulties of identifying pixels of interest (POI) and performing plot segmentation due to the required intensive manual operations. We developed a Python package, GRID (GReenfield Image Decoder), to overcome this limitation. With pixel-wise K-means cluster analysis, users can specify the number of clusters and choose the clusters representing POI. The plot grid patterns are automatically recognized by the POI distribution. The local optima of POI are initialized as the plot centers, which can also be manually modified for deletion, addition, or relocation. The segmentation of POI around the plot centers is initialized by automated, intelligent agents to define plot boundaries. A plot intelligent agent negotiates with neighboring agents based on plot size and POI distributions. The negotiation can be refined by weighting more on either plot size or POI density. All adjustments are operated in a graphical user interface with real-time previews of outcomes so that users can refine segmentation results based on their knowledge of the fields. The final results are saved in text and image files. The text files include plot rows and columns, plot size, and total plot POI. The image files include displays of clusters, POI, and segmented plots. With GRID, users are completely liberated from the labor-intensive task of manually drawing plot lines or polygons. The supervised automation with GRID is expected to enhance the efficiency of agricultural field experiments. View Full-Text
Keywords: segmentation; pixels of interest; field plots; UAV; satellite; high-throughput phenotyping segmentation; pixels of interest; field plots; UAV; satellite; high-throughput phenotyping
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MDPI and ACS Style

Chen, C.J.; Zhang, Z. GRID: A Python Package for Field Plot Phenotyping Using Aerial Images. Remote Sens. 2020, 12, 1697. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111697

AMA Style

Chen CJ, Zhang Z. GRID: A Python Package for Field Plot Phenotyping Using Aerial Images. Remote Sensing. 2020; 12(11):1697. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111697

Chicago/Turabian Style

Chen, Chunpeng J., and Zhiwu Zhang. 2020. "GRID: A Python Package for Field Plot Phenotyping Using Aerial Images" Remote Sensing 12, no. 11: 1697. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111697

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