Unmanned aerial system‐based high‐throughput phenotyping for plant breeding

Bhandari, Mahendra; Chang, Anjin; Jung, Jinha; Ibrahim, Amir M. H.; Rudd, Jackie C.; Baker, Shannon; Landivar, Jose; Landivar, Juan

doi:10.1002/ppj2.20058

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…Generally, most respondents preferred to learn new techniques directly from their colleagues and from protocols developed and shared within their research group or institution. Indeed this approach is gaining traction as evidenced by several recently published protocols (Kefauver, Araus and Buchaillot, 2019;Matias et al, 2022;Bhandari et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Unoccupied aerial systems adoption in agricultural research

Lachowiec,

Feldman,

Matias

et al. 2024

Preprint

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A comprehensive survey and subject-expert interviews conducted among agricultural researchers investigated perceived value and barriers to the adoption of unoccupied aerial systems (UAS) in agricultural research. The study involved 154 respondents from 21 countries representing various agricultural sectors. The survey identified three key applications considered most promising for UAS in agriculture: precision agriculture, crop phenotyping/plant breeding, and crop modeling. Over 80% of respondents rated UAS for phenotyping as valuable, with 47.6% considering them very valuable. Among the participants, 41% were already using UAS technology in their research, while 49% expressed interest in future adoption. Current users highly valued UAS for phenotyping, with 63.9% considering them very valuable, compared to 39.4% of potential future users. The study also explored barriers to UAS adoption. The most commonly reported barriers were the “High cost of instruments/devices or software” (46.0%) and the “Lack of knowledge or trained personnel to analyze data” (40.9%). These barriers persisted as top concerns for both current and potential future users. Respondents expressed a desire for detailed step-by-step protocols for drone data processing pipelines (34.7%) and in-person training for personnel (16.5%) as valuable resources for UAS adoption. The research sheds light on the prevailing perceptions and challenges associated with UAS usage in agricultural research, emphasizing the potential of UAS in specific applications and identifying crucial barriers to address for wider adoption in the agricultural sector.

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Section: Discussionmentioning

confidence: 99%

Unoccupied aerial systems adoption in agricultural research

Lachowiec,

Feldman,

Matias

et al. 2024

Preprint

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Section: Uav Data Collection and Processingmentioning

confidence: 99%

“…The overall image processing pipeline followed in this study is presented in Figure 4. CC was quantified by applying a binary classification algorithm to the orthomosaic images based on the RGB parameters to separate canopy from non-canopy objects [20,21]. The determination of canopy cover utilized a binary image generated through an analysis of color values in the red-green-blue (RGB) system using Canopeo Version 2.0, an ACT image analysis tool [20].…”

Section: Uav Data Collection and Processingmentioning

confidence: 99%

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Risal,

Niu,

Landivar-Scott

et al. 2024

Water

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The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

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“…The collected images Phantom 4 RTK, which is equipped with a one-inch COSMOS, a red-green-blue (RGB) camera, and a Real-Time Kinematics (RTK) module, was used to collect the UAS data. The procedure for image processing and feature extraction was followed as described by Bhandari et al [32]. The UAS flights were conducted at irregular intervals, with varying time gaps between each flight, ranging from 10 to 14 days.…”

Section: Data Collection and Feature Extractionmentioning

confidence: 99%

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Dhal,

Kalafatis,

Braga-Neto

et al. 2024

Remote Sensing

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Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

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Unmanned aerial system‐based high‐throughput phenotyping for plant breeding

Cited by 9 publications

References 38 publications

Unoccupied aerial systems adoption in agricultural research

Unoccupied aerial systems adoption in agricultural research

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

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