Impact of Plant Nutrients on Prediction of Wheat Crop Yield from Pond Ash Amended Field by Artificial Neural Networks

2019 ◽  
Vol 45 (1) ◽  
pp. 76-83
Author(s):  
R.C. Tripathi ◽  
V.K. Kalyani ◽  
S.K Jha ◽  
N.K. Srivastava ◽  
S.K. Thakur
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Plant Nutrients ◽  
Wheat Crop ◽  
Pond Ash ◽  
Artificial Neural

Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

2020 ◽  
pp. 030913332095663 ◽  
Author(s):  
Boyi Liang ◽  
Hongyan Liu ◽  
Timothy A Quine ◽  
Xiaoqiu Chen ◽  
Paul D Hallett ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Spatial Patterns ◽  
Crop Production ◽  
Guizhou Province ◽  
Crop Species ◽  
Environmental Controls ◽  
Artificial Neural ◽  
Propagation Network

The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.

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

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.

scholarly journals Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 609
Author(s):  
Patryk Hara ◽  
Magdalena Piekutowska ◽  
Gniewko Niedbała
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Climatic Data ◽  
Yield Prediction ◽  
Agricultural Crop ◽  
Independent Variables ◽  
Artificial Neural ◽  
Selection Of

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

scholarly journals Predicting the characteristics of pond ash concrete using artificial neural networks

2020 ◽  
Vol 872 ◽  
pp. 012176
Author(s):  
G Swamy Yadav ◽  
Dara Swetha Sudarshan ◽  
G Sahithi ◽  
E Laxmi Prasanna
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pond Ash ◽  
Artificial Neural
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