CanopyCAM – an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans

Wei zhen Liang, Joseph Oboamah, Xin Qiao, Yufeng Ge, Bob Harveson, Daran R. Rudnick, Jun Wang, Haishun Yang, Angie Gradiz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number107498
JournalComputers and Electronics in Agriculture
Volume204
DOIs
StatePublished - Jan 2023

Keywords

  • Canopy Cover (CC)
  • Edge computing
  • Image processing
  • Internet of Things (IoT)
  • Leaf area index (LAI)

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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