Journal article
CanopyCAM – an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans
Computers and electronics in agriculture, Vol.204, 107498
01/2023
DOI: 10.1016/j.compag.2022.107498
Abstract
•An image-based edge-computing device – CanopyCAM was developed to be able to monitor canopy cover of dry edible beans in real-time and continuously with high accuracy.•Important canopy cover features of dry edible beans such as maximum canopy cover (CC) and duration of maximum CC can be extracted based on continuous measurement of CC.
Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI) using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff timing to allow the crop to dry down for harvest. Therefore, the goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to process and transmit CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farm fields, respectively. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm in each day. Initially, the overall trend of CC development increased over time but fluctuations in daily readings were noticed due to changing lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCanopyCAM) were compared with CC obtained from a LI-COR Plant Canopy Analyzer (CCLAI). The average error between CCCanopyCAM and CCLAI was 2.3 %, and RMSE and R2 were 2.95 % and 0.99, respectively. In addition, maximum CC (CCmax) and duration of the maximum CC (tmax_canopy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCmax and tmax_canopy (R2 = 0.77, Adjusted R2 = 0.62) as compared to yield versus CCmax (R2 = 0.58) or yield versus tmax_canopy (R2 = 0.45) only. This edge-computing, IoT enabled CanopyCAM, provided accurate and continuous CC readings for dry edible beans which could be used by growers and researchers for different purposes.
Details
- Title: Subtitle
- CanopyCAM – an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans
- Creators
- Wei-zhen Liang - University of Nebraska–LincolnJoseph Oboamah - University of Nebraska–LincolnXin Qiao - University of Nebraska–LincolnYufeng Ge - University of Nebraska–LincolnBob Harveson - University of Nebraska–LincolnDaran R. Rudnick - University of Nebraska–LincolnJun Wang - University of IowaHaishun Yang - University of Nebraska–LincolnAngie Gradiz - University of Nebraska–Lincoln
- Resource Type
- Journal article
- Publication Details
- Computers and electronics in agriculture, Vol.204, 107498
- DOI
- 10.1016/j.compag.2022.107498
- ISSN
- 0168-1699
- eISSN
- 1872-7107
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 01/2023
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984322059602771
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