Crop yield prediction using a CMAC neural network

1994 ◽  
Author(s):  
George Simpson
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Yield Prediction ◽  
Cmac Neural Network

Crop Yield Prediction Using Deep Neural Network

2020 ◽  
Author(s):  
Fatin Farhan Haque ◽  
Ahmed Abdelgawad ◽  
Venkata Prasanth Yanambaka ◽  
Kumar Yelamarthi
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Yield Prediction

scholarly journals Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

2010 ◽  
Vol 2 (3) ◽  
pp. 673-696 ◽  
Author(s):  
Sudhanshu Sekhar Panda ◽  
Daniel P. Ames ◽  
Suranjan Panigrahi
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Vegetation Indices ◽  
Yield Prediction ◽  
Agricultural Crop

scholarly journals Multi-Model Ensemble Depth Adaptive Deep Neural Network for Crop Yield Prediction

2020 ◽  
Vol 8 (5) ◽  
pp. 3088-3093
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Minimum Weight ◽  
Back Propagation ◽  
Soil Parameters ◽  
Yield Prediction ◽  
Model Ensemble ◽  
Training Scheme ◽  
Online Setting

Accurate prediction of crop yield enables critical tasks such as identifying the optimum crop profile for planting, assigning government resources and decision-making on imports and exports in more commercialized systems. In past few years, Machine Learning (ML) techniques have been widely used for crop yield prediction. Deep Neural Network (DNN) was introduced for crop yield. The crop yield prediction accuracy based on DNN was further improved by Multi-Model DNN (MME-DNN). It predicted the crop yield by modeling climatic, weather and soil parameters through statistical model and DNN. The MME-DNN is not scalable when new data appears consecutively in a stream form. In order to solve this problem, an Online Learning (OL) is introduced for crop yield prediction. In OL, DNN is learned in an online setting which optimizes the objective function regarding shallow model. But, a fixed depth of the network is used in ODL and it cannot be changed during the training process. So, Multi-Model Ensemble Depth Adaptive Deep Neural Network (MME-DADNN) is proposed in this paper to adaptively decide the depth of the network for crop yield prediction. A training scheme for OL is designed through a hedge back propagation. It automatically decides the depth of the DNN using Online Gradient Descent (OGD) in an online manner. Also, a smoothing parameter is introduced in OL to set a minimum weight for every depth of DNN and it also contributes a balance between exploitation and exploration. The crop yield is predicted from the soil, weather and climate parameters and their variation over four years by applying the MME-DADNN. Thus, by adaptively changing the depth of the DNN the performance of crop yield prediction is enhanced.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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