Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

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Modelling nitrogen content of pasture herbage using thermal images and artificial neural networks

Thermal Science and Engineering Progress ◽

10.1016/j.tsep.2019.04.005 ◽

2019 ◽

Vol 11 ◽

pp. 283-288 ◽

Cited By ~ 1

Author(s):

M. Safa ◽

K.E. Martin ◽

B. KC ◽

R. Khadka ◽

T.M.R. Maxwell

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Nitrogen Content ◽

Thermal Images ◽

Artificial Neural

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Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2014040103 ◽

2014 ◽

Vol 5 (2) ◽

pp. 38-49

Author(s):

Claudio Kapp Junior ◽

Eduardo Fávero Caires ◽

Alaine Margarete Guimarães

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Biomass Production ◽

Precision Agriculture ◽

Spectral Reflectance ◽

Wheat Crop ◽

Nitrogen Status ◽

Artificial Neural ◽

N Rates

Precision Agriculture has the goal of reducing cost which is difficult when it is related to fertilizers application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizers application present significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the Resilient Propagation and Backpropagation Artificial Neural Networks algorithms. As a result was detected a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants. For both algorithms it was established a satisfactory model for estimating wheat dry biomass production. The best Backpropagation and Resilient Propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

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Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

Water ◽

10.3390/w13213094 ◽

2021 ◽

Vol 13 (21) ◽

pp. 3094

Author(s):

Salah Elsayed ◽

Hekmat Ibrahim ◽

Hend Hussein ◽

Osama Elsherbiny ◽

Adel H. Elmetwalli ◽

...

Keyword(s):

Neural Networks ◽

Water Quality ◽

Artificial Neural Networks ◽

Spectral Reflectance ◽

Large Scale ◽

Sampling Point ◽

Calibration And Validation ◽

Artificial Neural ◽

Reflectance Indices ◽

Spectral Reflectance Indices

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R2 with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH4+, and PO43−) with (R2 = 0.70 to 0.77), and a moderate relationship with COD (R2 = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R2 values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO43−VI-17 was the highest accuracy model for predicting PO43− with R2 = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.

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L-system models for image-based phenomics: case studies of maize and canola

in silico Plants ◽

10.1093/insilicoplants/diab039 ◽

2021 ◽

Author(s):

Mikolaj Cieslak ◽

Nazifa Khan ◽

Pascal Ferraro ◽

Raju Soolanayakanahally ◽

Stephen J Robinson ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Case Studies ◽

Crop Plants ◽

Great Promise ◽

Attractive Alternative ◽

Manual Annotation ◽

L System ◽

Artificial Neural ◽

Real Plant

Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.

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