A research team has developed a cost-effective, high-throughput imaging method using a high-definition digital camera and Canopeo software to accurately estimate cotton plant height and above-ground biomass. This approach, validated across various cotton genotypes and irrigation levels, offers a non-destructive, accessible tool for monitoring crop growth, potentially enhancing intelligent farmland management. Its ease of use allows ordinary farmers and researchers to efficiently assess crop phenotypes, paving the way for broader applications in crop monitoring and precision agriculture.
Traditional phenotypic analysis methods are labor-intensive, time-consuming, and inefficient, making them unsuitable for large-scale farmland. While recent advancements in high-throughput phenotypic analysis, particularly digital image analysis, have improved efficiency, current techniques often focus on nutritional diagnostics rather than comprehensive crop monitoring. Cotton is an important cash crop that is widely cultivated worldwide. However, the feasibility of efficient and non-destructive crop phenotypic monitoring technologies for estimating cotton plant height and above-ground biomass has not yet been determined.
A research article (DOI: 10.48130/TIA-2022-0004) published in Technology in Agronomy on 20 December 2022, investigates the feasibility of using Canopeo imaging technology to efficiently estimate cottot (PH) and above-ground biomass (AGB).
The study proposed using Canopeo technology to extract green color percentages from high-definition digital images and establish a model to estimate cotton plant height and above-ground biomass. Firstly, the analysis of plant height (PH), above-ground biomass (AGB), and percentages of green color (PGC) using 80 cotton genotypes under drought stress (DS) and control check (CK) treatments revealed that the average values of PGC, PH, and AGB significantly decreased (P < 0.0001) under DS compared to CK. The Bayesian multivariate mixed model confirmed strong positive correlations between PGC, PH, and AGB, with correlation coefficients exceeding 0.96 (P < 0.01). Then, the linear fitting performed well across the different cotton genotypes (PH, R2 = 0.9829; RMSE = 2.4 cm; NRMSE = 11% and AGB, R2 = 0.9609; RMSE = 0.6 g / plant; and NRMSE = 5%) and two levels of irrigation (PH, R2 = 0.9604; RMSE = 2.15 cm; NRMSE = 6% and AGB, R2 = 0.9650; RMSE = 4.51 g/plant; and NRMSE = 17%). Additionally, the most comprehensive model was developed with equations Y = 0.4832*X + 11.04 for PH and Y = 0.4621*X – 0.3591 for AGB. The PGC positively correlated with PH and AGB, and each model exhibited higher accuracy (R2 ≥ 0.8392, RMSE ≤ 0.0158, NRMSE ≤ 0.06%). Thus, the digital imaging technology and extracted PGC can serve as effective tools and indirect estimates of cotton growth, respectively. While Canopeo provided a cost-effective, non-destructive tool for large-scale crop monitoring, the study highlighted potential discrepancies between predicted and actual values due to canopy effects and emphasized the need for standardized operational methods to enhance the robustness and accuracy of this phenotypic analysis approach.
According to the study’s lead researcher, Cundong Li, “The technique does not require professional knowledge of computer and machine learning and thus, can be utilized by ordinary farmers or researchers.”
In summary, this study developed a low-cost, high-throughput imaging method using Canopeo and digital cameras to estimate cotton PH and AGB with high accuracy across various genotypes and irrigation levels. The approach offers a non-destructive, efficient tool for crop monitoring, accessible to both researchers and farmers. Looking ahead, this method could be adapted to measure other important crop phenotypes, supporting the advancement of precision agriculture and sustainable farming practices.
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References
DOI
Original Source URL
https://doi.org/10.48130/TIA-2022-0004
Funding information
This study was supported by grants from the National Natural Science Foundation of China (No. 31871569 and No. 32172120), Natural Science Foundation of Hebei Province (C2020204066), and the Modern System of Agricultural Technology in Hebei Province (No. HBCT2018040201).
About Technology in Agronomy
Technology in Agronomy (e-ISSN 2835-9445) is an open access, online-only academic journal sharing worldwide research in breakthrough technologies and applied sciences in agronomy. Technology in Agronomy publishes original research articles, reviews, opinions, methods, editorials, letters, and perspectives in all aspects of applied sciences and technology related to production agriculture, including (but not limited to): agronomy, crop science, soil science, precision agriculture, and agroecology.
