Using the artificial neural network to estimate leaf area

Shabani, Ali; Ghaffary, Keramat Allah; Sepaskhah, Ali Reza; Kamgar-Haghighi, Ali Akbar

doi:10.1016/j.scienta.2016.12.032

Cited by 42 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The present study did not compare the PLS algorithm with artificial neural network (ANN) [50], support vector machines (SVM) [51], geostatistics [52], etc. Simultaneously, it also did not take into account the factors affecting winter wheat cultivation such as weather, soil and cultivation practices and so on.…”

Section: Plos Onementioning

confidence: 99%

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm

et al. 2020

View full text Add to dashboard Cite

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soiladjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), R blue (reflectance at blue band), R green (reflectance at green band), R red (reflectance at red band) and R nir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than R blue and R green of the spectral reflectance bands. Among them, except PSRI and R blue , R green and R red , other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R 2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than

show abstract

Section: Plos Onementioning

confidence: 99%

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The present study did not compare the PLS algorithm with artificial neural network (ANN) 43,44 , support vector machines (SVM) 45 , geostatistics 46 , etc. Simultaneously, it also did not take into account the factors affecting winter wheat cultivation.…”

Section: Discussionmentioning

confidence: 99%

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Zhang

Zhou

Wang

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R 2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha −1 and 786.5 kg ha −1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion. Scientifically and accurately estimating crop yield is of significant importance for formulating plans for social and economic development, determining agricultural products import and export plans, ensuring national food security, guiding and regulating macroscopic planting structure, as well as improving the management skills of relevant agriculture-related enterprises and farmers 1-6. With the improvement of spatial, temporal and spectral resolutions of remote sensing data and the significant reduction of cost, currently remote sensing has been widely used in the estimation of production of all kinds of food crops, and it has become a research focus in the interdisciplinary field combining remote sensing and agriculture 7. At present, there were many methods and means for estimating crop yield, such as crop yield meteorological forecast, artificial sampling survey, statistical simulation model, remote sensing estimation and so on 8,9. Using a Criteria/Wofost simulation model that included the new numerical scheme for soil water balance, some researchers compared field data collected at the university of bologna's experimental farm in 1977-1987 with the median wheat yield, and the predicted value was consistent with the observed value 10. Other researches have suggested that the mars-crop ...

show abstract

“…In this study, we used 70% of the data randomly to train the ANN model, 15% of the data were used to verify the ANN model, and the remaining 15% of the data were used to test the ANN model. The following equation was used to normalize the data (Shabani et al., 2017):

T_{n} = \frac{T ‐ T_{truemin}}{T_{truemax} ‐ T_{truemin}}

…”

Section: Methodsmentioning

confidence: 99%

Using artificial neural network in predicting the key fruit quality of loquat

Huang

Wang²,

et al. 2021

Food Science & Nutrition

View full text Add to dashboard Cite

The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12–12–1 can predict the single fruit weight with the highest accuracy (R2 = .91), the ANN model with the structure of 10–11–1 can predict the soluble solid content with the highest accuracy (R2 = .91), and the ANN model with the structure of 9–10–1 can predict the titratable acid content with the highest accuracy (R2 = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.

show abstract

Using the artificial neural network to estimate leaf area

Cited by 42 publications

References 19 publications

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Using artificial neural network in predicting the key fruit quality of loquat

Contact Info

Product

Resources

About