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.

scholarly journals How Can We Realize Sustainable Development Goals in Rocky Desertified Regions by Enhancing Crop Yield with Reduction of Environmental Risks?

2021 ◽  
Vol 13 (9) ◽  
pp. 1614
Author(s):  
Boyi Liang ◽  
Timothy A. Quine ◽  
Hongyan Liu ◽  
Elizabeth L. Cressey ◽  
Ian Bateman
Keyword(s):  
Sustainable Development ◽  
Crop Yield ◽  
Crop Production ◽  
Sustainable Development Goals ◽  
Positive Impact ◽  
Total Yield ◽  
Maximum Yield ◽  
Guizhou Province ◽  
Crop Species ◽  
Development Goals

To meet the sustainable development goals in rocky desertified regions like Guizhou Province in China, we should maximize the crop yield with minimal environmental costs. In this study, we first calculated the yield gap for 6 main crop species in Guizhou Province and evaluated the quantitative relationships between crop yield and influencing variables utilizing ensembled artificial neural networks. We also tested the influence of adjusting the quantity of local fertilization and irrigation on crop production in Guizhou Province. Results showed that the total yield of the selected crops had, on average, reached over 72.5% of the theoretical maximum yield. Increasing irrigation tended to be more consistently effective at increasing crop yield than additional fertilization. Conversely, appropriate reduction of fertilization may even benefit crop yield in some regions, simultaneously resulting in significantly higher fertilization efficiency with lower residuals in the environment. The total positive impact of continuous intensification of irrigation and fertilization on most crop species was limited. Therefore, local stakeholders are advised to consider other agricultural management measures to improve crop yield in this region.

scholarly journals Sensitivity analysis of energy inputs in crop production using artificial neural networks

2018 ◽  
Vol 197 ◽  
pp. 992-998 ◽  
Author(s):  
Alireza Khoshroo ◽  
Ali Emrouznejad ◽  
Ahmadreza Ghaffarizadeh ◽  
Mehdi Kasraei ◽  
Mahmoud Omid
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Crop Production ◽  
Energy Inputs ◽  
Artificial Neural

Prediction of Bubble Point Pressure Using Artificial Neural Networks in the Niger Delta

2020 ◽  
Vol 11 (01) ◽  
pp. 1445-1454
Author(s):  
Uduma U. Idika
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Niger Delta ◽  
Reservoir Temperature ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure ◽  
Data Points ◽  
Artificial Neural ◽  
Propagation Network

A model was developed to predict the bubble point pressure of saturated reservoirs. The model was based on artificial neural networks and was developed using 700 generic data sets which are representative of the Niger Delta region of Nigeria. The data set was first cleaned to remove erroneous and repeated data points. After cleaning, 618 data points were remaining. Of the 618 data points, 463 were used to train the ANN model, 93 were used to cross-validate the relationships established during the training process and the remaining 62 were used to test the model to evaluate its accuracy. A backward propagation network utilizing the LM algorithm was used in developing the model. The first layer consisted of four neurons representing the input values of reservoir temperature, API oil gravity, gas specific gravity, and solution GOR. The second (hidden) layer consisted of 26 neurons, and the third layer contained one neuron representing the output value of the bubble point pressure. The results showed that the developed model provides better predictions and higher accuracy than the existing empirical correlations considered when exposed to an additional 13 data points which were unseen by the model during its development. The model provided predictions of the bubble point pressure with an absolute average percent error of 3.98%, RMSE of 177.6479 and correlation coefficient of 0.9851. Trend analysis was performed to check the behavior of the predicted values of P_b for any change in reservoir temperature, oil API gravity, gas gravity and solution GOR. The model was found to be physically correct. Its stability indicated that it did not overfit the data, implying that it was successfully trained.

Applying Artificial Neural Networks: Part II. Using near Infrared Data to Classify Tobacco Types and Identify Native Grown Tobacco

1997 ◽  
Vol 5 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Maha Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M.W. White ◽  
D.R. Bahler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Near Infrared ◽  
Back Propagation ◽  
Calibration Model ◽  
Linear Network ◽  
Burley Tobacco ◽  
The Usa ◽  
Artificial Neural ◽  
Propagation Network

Classification of flue-cured and Burley tobacco types with artificial neural networks (ANNs) were studied. Burley tobacco was further classified as either grown in the USA or grown outside the USA. The input data were in the form of near infrared (NIR) spectra, each spectrum containing 19 points. The number of flue-cured and Burley samples were 654 and 959, respectively. The number of native and non-native tobacco samples were 266 and 267, respectively. The models selected for this research were a quadratic classifier, a back-propagation network and a linear network. The results of the calibration model and the true performance for classifying tobacco species were (100%, 100%), (99.38%, 99.39%) and (95.19%, 99.26%) for the quadratic classifier, back-propagation network and linear network, respectively. The identification of native tobacco and its true performance were (100%, 100%) using a quadratic classifier, (89.12%, 88.46%) using a back-propagation network and (80.68%, 79.62%) using a linear network.

scholarly journals ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohammed H Adnan ◽  
Mustafa Muneer Isma’eel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Exchange ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

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