Rice Crop Yield Prediction Using Random Forest and Deep Neural Network - An Integrated Approach

2021 ◽  
Author(s):  
Banu E ◽  
Geetha A
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Integrated Approach ◽  
Rice Crop ◽  
Yield Prediction

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 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.

Series Arc Fault Detection Based on Random Forest and Deep Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Jun Jiang ◽  
Wei Li ◽  
Zhe Wen ◽  
Yifan Bie ◽  
Harald Schwarz ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Fault Detection ◽  
Deep Neural Network

Palm Vein Classification Based on Deep Neural Network and Random Forest

2019 ◽  
Vol 56 (10) ◽  
pp. 101010
Author(s):  
袁丽莎 Yuan Lisha ◽  
娄梦莹 Lou Mengying ◽  
刘娅琴 Liu Yaqin ◽  
杨丰 Yang Feng ◽  
黄靖 Huang Jing
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Deep Neural Network ◽  
Palm Vein

scholarly journals Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

2019 ◽  
Vol 11 (13) ◽  
pp. 1584 ◽  
Author(s):  
Yang Chen ◽  
Won Suk Lee ◽  
Hao Gan ◽  
Natalia Peres ◽  
Clyde Fraisse ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection System ◽  
Growth Cycle ◽  
Aerial Images ◽  
Accurate Estimation ◽  
Yield Prediction ◽  
Distribution Maps ◽  
Detection And Counting

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

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
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