scholarly journals DISTRICT LEVEL WHEAT YIELD PREDICTION FROM COARSE RESOLUTION SATELLITE DATA USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Author(s):  
Sharad Gupta ◽  
Suman Kumari ◽  
Sagar Taneja
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Wheat Yield ◽  
District Level ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Coarse Resolution ◽  
Learning Techniques

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

2020 ◽  
Author(s):  
Victor Bacu ◽  
Teodor Stefanut ◽  
Dorian Gorgan
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Field Measurements ◽  
Soil Classification ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Spatial Features ◽  
Learning Techniques ◽  
Sentinel 2

<p>Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.</p><p>Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).</p><p>The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.</p><p> </p><p>References:</p><p>[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., “Soil Classification Techniques in Transylvania Area Based on Satellite Data”. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).</p><p>[2] Bacu V., Stefanut T., Gorgan D., “Building soil classification maps using HorusApp and Sentinel-2 Products”, Proceedings of the Intelligent Computer Communication and Processing Conference – ICCP, in IEEE press (2019).</p><p>[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., “Soil classification based on Sentinel-2 Products using HorusApp application”, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).</p>

scholarly journals ML Methods for Crop Yield Prediction and Estimation: An Exploration

2020 ◽  
Vol 9 (3) ◽  
pp. 3506-3508
Keyword(s):  
Machine Learning ◽  
Detailed Analysis ◽  
Crop Yield ◽  
Learning Process ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Yield Estimation ◽  
Data Set ◽  
Yield Optimization ◽  
Learning Techniques

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

scholarly journals Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Climate ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 5 ◽  
Author(s):  
Vivek Shandas ◽  
Jackson Voelkel ◽  
Joseph Williams ◽  
Jeremy Hoffman
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Metropolitan Region ◽  
Landscape Features ◽  
Ambient Temperatures ◽  
Air Temperatures ◽  
Learning Techniques ◽  
Urban Heat

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

scholarly journals Review of Crop Yield Prediction using Machine Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 4626-4628
Author(s):  
Kale Jaydeep Narayan
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Soil Types ◽  
Yield Prediction ◽  
Growth Season ◽  
Learning Techniques ◽  
Agricultural Yield ◽  
Decision Making Tool

Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.

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