Rice crop yield prediction using artificial neural networks

2016 ◽  
Author(s):  
Niketa Gandhi ◽  
Owaiz Petkar ◽  
Leisa J. Armstrong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Rice Crop ◽  
Yield Prediction ◽  
Artificial Neural

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.

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 Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

2019 ◽  
Vol 11 (2) ◽  
pp. 533 ◽  
Author(s):  
Gniewko Niedbała
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Approximation Error ◽  
Main Element ◽  
Forecast Errors ◽  
Yield Prediction ◽  
Winter Rapeseed ◽  
Artificial Neural

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

scholarly journals Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

2021 ◽  
Vol 51 ◽  
Author(s):  
Bruno Vinícius Castro Guimarães ◽  
Sérgio Luiz Rodrigues Donato ◽  
Ignacio Aspiazú ◽  
Alcinei Mistico Azevedo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Efficiency ◽  
Prediction Models ◽  
Ground Level ◽  
Coefficient Of Determination ◽  
Yield Prediction ◽  
Average Mass ◽  
Artificial Neural ◽  
Mean Square Errors

ABSTRACT Prediction models may contribute to data analysis and decision-making in the management of a crop. This study aimed to evaluate the feasibility of predicting the yield of ‘Prata-Anã’ and ‘BRS Platina’ banana plants by means of artificial neural networks, as well as to determine the most important morphological descriptors for this purpose. The following characteristics were measured: plant height; perimeter of the pseudostem at the ground level, at 30 cm and 100 cm; number of live leaves at harvest; stalk mass, length and diameter; number of hands and fruits; bunches and hands masses; hands average mass; and ratio between the stalk and bunch masses. The data were submitted to artificial neural networks analysis using the R software. The best adjustments were obtained with two and three neurons at the intermediate layer, respectively for ‘Prata-Anã’ and ‘BRS Platina’. These models presented the lowest mean square errors, which correspond to the higher proximity between the predicted and the real data, and, therefore, a higher efficiency of the networks in the yield prediction. By the coefficient of determination, the best adjustments were found for ‘Prata-Anã’ (R² = 0.99 for all the network compositions), while, for ‘BRS Platina’, the data adjustment enabled an R² with values between 0.97 and 1.00, approximately. Yield predictions for ‘Prata-Anã’ and ‘BRS Platina’ were obtained with high efficiency by using artificial neural networks.

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