An Innovative method for NDVI correction and cloud removal in Sugarcane remote sensing

Clouds and atmospheric disturbances reduce the accuracy of satellite-based NDVI analysis in sugarcane monitoring. This study uses a minimum image reducer method to remove cloud effects, improve vegetation clarity, and support accurate crop classification, monitoring, and yield prediction.

Mansi Manoj Kambli

May 14, 2026

Multimedia Tools and Applications

Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop

The success of sugarcane development is the main subject of this work, which combines a variety of machine learning techniques to remotely sense sugarcane crop data and distinguish between dense and sparse vegetation.

Mansi Manoj Kambli

Jun 06, 2025

Springer

Deep learning models for remotely sensed data of sugarcane

The traditional machine learning algorithms are giving way to approaches for deep learning in computer vision, which refers to a computer's capacity to infer meaning from digital images and videos. Sugarcane categorization is important for agricultural management and monitoring.

Mansi Manoj Kambli

Jan 09, 2025

sugarcane categorisation using Deep learning models on Sentinel-2 dataset

This paper’s primary goal is to classify sugarcane crops using deep learning models using Sentinel imaging data.. The classification aids farmers in making prompt judgements and managing their crops for maximum production.

Mansi Manoj Kambli

Feb 22, 2025

Mansi Manoj Kambli

Popular Content

An Innovative method for NDVI correction and cloud removal in Sugarcane remote sensing

Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop

Deep learning models for remotely sensed data of sugarcane

sugarcane categorisation using Deep learning models on Sentinel-2 dataset

Online Elsewhere

Mansi Manoj Kambli

Popular Content

An Innovative method for NDVI correction and cloud removal in Sugarcane remote sensing

Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop

Deep learning models for remotely sensed data of sugarcane

sugarcane categorisation using Deep learning models on Sentinel-2 dataset

Online Elsewhere

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