Crop Yield Prediction Using Deep Learning in Mediterranean Region

2020 ◽  
pp. 106-114
Author(s):  
Maroi Tsouli Fathi ◽  
Mostafa Ezziyyani ◽  
Mohammed Ezziyyani ◽  
Soumaya El Mamoune
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Mediterranean Region ◽  
Yield Prediction

scholarly journals A Review Paper on Deep Learning Approach for Crop Yield Prediction Assessment

2021 ◽  
Vol 7 (05) ◽  
pp. 127-132
Author(s):  
Richa Verma & Ayushi
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Review Paper ◽  
Learning Approach ◽  
Yield Prediction ◽  
Learning Methods ◽  
Remote Sensor ◽  
The Cross ◽  
Precise Assessment

Precise assessment of harvest yield is a difficult field of work. The equipment and programming stage to foresee the harvest yield relies on different components like climate, soil fruitfulness, genotype, and different collaborating wards. The assignment is unpredictable inferable from the information that should be gathered in volumes to comprehend crop yield through remote sensor organizations and distant detecting. This paper audits the previous 15 years of exploration work in the improvement of assessing crop yield utilizing profound learning calculations. The meaning of examining progressions utilizing profound learning methods will help in dynamic for foreseeing the harvest yield. The cross breed mix of profound learning with distant detecting and remote sensor organizations can give accuracy agribusiness later on.

scholarly journals Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

2021 ◽  
Author(s):  
Amit Kumar Srivast ◽  
Nima Safaei ◽  
Saeed Khaki ◽  
Gina Lopez ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Learning Models ◽  
Winter Wheat Yield ◽  
Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning

2021 ◽  
Vol 174 ◽  
pp. 265-281
Author(s):  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
Matthew Maimaitiyiming ◽  
Davis R. Brown ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Yield Prediction ◽  
Field Scale ◽  
Multi Temporal
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