A Comparative Study of Three Supervised Algorithms for Mixed Crop Classification
E3S Web of Conferences, ISSN: 2267-1242, Vol: 590
2024
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
This study focuses on advancing precision agriculture through machine learning algorithms applied to crop classification using PlanetScope multispectral data in Kheda district, Gujarat. Three algorithms—Support Vector Machines (SVM), Spectral Angle Mapper (SAM), and Random Forests (RF)—were tested for their accuracy in classifying crop types. Additionally, the research utilized multi-temporal satellite imagery to monitor crop phenological cycles, enhancing classification reliability. The results highlighted SVM's boundary delineation, SAM's spectral similarity approach, and RF's ensemble learning as effective in distinguishing crops in mixed scenarios. Integrating ground truth data further validated classification accuracy, underscoring the study's contribution to improving agricultural management and planning.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know