Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery

Hou, Mengjie; Tian, Fei; Zhang, Tong; Huang, Mengsi

doi:10.1016/j.agwat.2019.06.005

Cited by 28 publications

(12 citation statements)

References 57 publications

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“…Such difference could be due to the low energy input in the controlled environment growth room, the crop being grown in a hydroponic setup, minimal heat waste from the light source (LED lights) and constant air circulation in the growth room. In previous studies, CTD values of 7.5, 10, and 12 • C have been reported for sorghum (Zhang et al, 2019), alfalfa (Moran et al, 1994) and soybean (Hou et al, 2019), respectively.…”

Section: Discussionmentioning

confidence: 84%

Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens

Tavan

Wee

Brodie

et al. 2021

Front. Sustain. Food Syst.

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With water resources constantly becoming scarcer, and 70% of freshwater used for the agriculture sector, there is a growing need for innovative methods to increase water use efficiency (WUE) of food production systems and provide nutrient-dense food to an increasing population. Sensor technology has recently been introduced to the horticulture industry to increase resource use efficiency and minimize the environmental impacts of excessive water use. Identifying the effects of irrigation levels on crop performance is crucial for the success of sensor-based water management. This research aimed to optimize WUE in a soilless microgreen production system through identification of an optimal irrigation level using a sensor that could facilitate the development of a more efficient, low-cost automated irrigation system. A dielectric moisture sensor was implemented to monitor water levels at five irrigation setpoints: 7.5, 17.5, 25, 30, and 35 percent of the effective volume of the container (EVC) during a 14-day growth cycle. To validate the sensor performance, the same irrigation levels were applied to a parallel trial, without sensor, and water levels were monitored gravimetrically. Plant water status and stress reaction were evaluated using infrared thermal imaging, and the accumulation of osmolytes (proline) was determined. Results showed that, proline concentration, canopy temperature (Tc), canopy temperature depression (CTD), and crop water stress index (CWSI) increased at 7.5% EVC in both sensor-based and gravimetric treatments, and infrared index (Ig) and fresh yield decreased. The dielectric moisture sensor was effective in increasing WUE. The irrigation level of 17.5% EVC was found to be optimal. It resulted in a WUE of 88 g/L, an improvement of 30% over the gravimetric method at the same irrigation level. Furthermore, fresh yield increased by 11.5%. The outcome of this study could contribute to the automation of precision irrigation in hydroponically grown microgreens.

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Section: Discussionmentioning

confidence: 84%

Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens

Tavan

Wee

Brodie

et al. 2021

Front. Sustain. Food Syst.

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“…Crop water management is essential in regions currently facing or predicted to face water scarcity. Infrared thermography has been successfully implemented to assess water use by crops ( Nhamo et al, 2020 ), and for measuring genotype performance under salinity or water deficit stress ( Raza et al, 2014 ; Kumar et al, 2017 ; Thapa et al, 2018 ; Hou et al, 2019 ; Zhang et al, 2019b ; Kumar et al, 2020 ; Masina et al, 2020 ). In cotton ( Gossypium arboreum ) monitored by infrared thermography, it was observed that yield, fiber length, and micronaire suffered reduction after canopy temperature exceeded a given threshold ( Conaty et al, 2015 ).…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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“…The linear regression models have been used in many previous yield estimation studies to indicate a linear or exponential relationship to single or multiple variables (Hou et al 2019;Gaso, Berger, and Ciganda 2019). The slope and intercept are the two parameters that describe the linear relationship between the dependent and independent variables.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Zhou

Ismaeel

2021

Geo-spatial Information Science

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Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R 2 ), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6-8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.

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Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery

Cited by 28 publications

References 57 publications

Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens

Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

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