Sentence examples for classification of crop from inspiring English sources

Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes.

In recent years much research in the field of classification and classification of crops based on characteristics using image processing have been done in this study to determine the level of product quality barberry impurities and the degree of quality we have to use image processing.

In the presented study, possibilities for supervised multitemporal classification of non crop areas are investigated based on TerraSAR-X images and two different classifiers (Maximum Likelihood and Random Forest).

Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data.

The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt.

Generally, the overall accuracy of crop classification in Zhangye was high, at 89.38%.

This study shows progress toward more refined crop-specific classification, but some grouping of crop classes remains necessary.

The objective of this study is to analyze the accuracy of crop classification and area estimation affected by spatial heterogeneities, especially for sample impurity and landscape heterogeneity.

The LCRAS accuracy assessment was designed to quantify the impact of crop classification error on annual total crop evapotranspiration (ETc), as calculated from the Penman Monteith method using the map crop classification as input.

In this study, the performance of the subspace method in land cover classification of a complex cropping mix area is explored.

This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data.