IEEE

Comparative Performance of ANN Predicting Soybean Yields from NDVI: Execution Time and CPU Usage in C and Python

Abstract: Artificial Neural Networks (ANNs) are a widely used and powerful tool for modeling and predicting various processes across society, industry, and nature. In recent decades, ANNs have found ...
SciELO

RESEARCH ON REMOTE SENSING CROP CLASSIFICATION BASED ON IMPROVED U-NET

For the classification and zoning study of crops at Jianshan Farm, data were acquired by integrating Sentinel-2 optical satellite imagery with Sentinel-1 radar satellite imagery. Different crop ...
Frontiers

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, ...
IEEE

A Novel Fusion Method for Soybean Yield Prediction Using Sentinel-2 and PlanetScope Imagery

Abstract: This study aimed to develop a new method for combining Sentinel-2 and PlanetScope (PS) imagery. The normalized difference vegetation indices (NDVI) data were retrieved from the Earth ...
SciELO

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System

The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great ...
GitHub

NDVI computation fails #17

{ "process_graph": { "loadco1": { "process_id": "load_collection", "arguments": { "id": "SENTINEL2_L2A", "spatial_extent": { "west": 7.6092989782618545, "east": 7. ...