Neural computing modelling of the crop water stress index

Kumar, Navsal; Adeloye, Adebayo J.; Shankar, Vijay; Rustum, Rabee

doi:10.1016/j.agwat.2020.106259

Cited by 34 publications

(15 citation statements)

References 40 publications

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“…The water stress gradient was set as five groups based on the calculated SFCP in this experiment [13], i.e., T1, T2, T3, T4, and T5 with 100, 85, 70, 55, and 40% SFCP, respectively [23], on the stability of non-water-stressed baselines. The two different methods were applied to calculate CWSI, i.e., a non-transpiration baseline and fixed ∆T methods for the upper limit of ∆T and a non-water-stress baseline for the lower limit of ∆T [24]. The upper and lower limits of ∆T were base parameters of CWSI based on empirical equations [25].…”

Section: Water Stress Treatment and Cwsi Calculating Methodsmentioning

confidence: 99%

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

et al. 2021

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Water stress is a significant element impacting photosynthesis, which is one of the major physiological activities governing crop growth and development. In this study, the photosynthetic rate of Brassica chinensis L. var. parachinensis (Bailey) (referred to as Chinese Brassica hereafter) was predicted using the deep learning method. Five sets of Chinese Brassica were created, each with a different water stress gradient. Air temperature (Ta), relative humidity (RH), canopy temperature (Tc), transpiration rate (Tr), photosynthetic rate (Pn), and photosynthetically available radiation (PAR) were measured in different growth stages. The upper limit and lower limit equations were built using the non-water-stress baseline (NWSB) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) methods. The crop water stress index (CWSI) was then calculated using these built equations. The multivariate long short-term memory (MLSTM) model was proposed to predict Pn based on CWSI and other parameters. At the same time, the support vector regression (SVR) method was applied to provide a comparison to the MSLTM model. The results show that water stress had an important effect on the growth of Chinese Brassica. The more serious the water stress, the lower the growth range (GR). The HDBSCAN method had a lower root mean square error (RMSE) in calculating CWSI. Furthermore, the CWSI had a significant effect on predicting Pn. The regression fitting between measured Pn and predicted Pn showed that the determination coefficient (R2) and RMSE were 0.899 and 0.108 μmol·m−2·s−1, respectively. In this study, we successfully developed a method for the reliable prediction of Pn in Chinese Brassica, which can serve as a useful reference for application in water saving.

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Section: Water Stress Treatment and Cwsi Calculating Methodsmentioning

confidence: 99%

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

et al. 2021

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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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“…Neural Network training is the process of calibrating the value of weights and biases of the network in order to perform the desired function [52]. Training can be classified into supervised and unsupervised [53]. In supervised training, the input and output data are present together.…”

Section: Figure 11 Neurons Structure With Biasmentioning

confidence: 99%

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

2021

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Solar radiation mapping has used geographical and meteorological data. To obtain geographical and meteorological data, a Geographic Information System (GIS) is required. GIS is defined as an integrated geographic resource that presents data in terms of spatial information. This data is important for Neural Networks as it will be used as input parameters for the development of solar radiation prediction models. Solar radiation prediction is one way to map the sun's rays in certain places where there are insufficient resources or space to build a complete solar radiation measurement station. Since predictions about solar radiation require meteorological and geographical data, this paper will give an overview of GIS-assisted geographical and meteorological data to be used as input parameters for solar radiation mapping which will eventually be used as input for prediction models developed for the whole country of Malaysia using Neural Networks. Based on the results, the prediction model developed managed to obtain a coefficient of determination, R2 value of 0.9329.

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Neural computing modelling of the crop water stress index

Cited by 34 publications

References 40 publications

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

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

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

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