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

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.

Plant phenotyping relevance

乾燥インゲンの群落被覆率を画像から連続推定するCanopyCAMを開発し、既存測定器との比較検証および精度評価を行っており、植物形質取得法が中心的である。

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