Deep Learning Models for Classification of 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. Traditional crop categorization methods based on manual inspection or restricted ground-based data gathering are time-consuming and frequently inaccurate. As a result, an automated and efficient strategy is suggested that requires the use of remote sensing data and the capabilities of deep learning algorithms. A dataset made from multispectral Sentinel imagery is used for the classification of sugarcane. This approach seeks to separate sugarcane-growing regions from other regions in Sentinel-2 images using VGG19, MobileNetV2, and CNN as feature extractors. These findings illustrate the feature extraction utilizing deep learning models with an SVM classifier for sugarcane. By considering variables such as distinct spectral bands, temporal fluctuations, and potential difficulties in separating sugarcane from other land cover types, the objective is to construct and check working of deep learning models for categorizing sugarcane locations using Sentinel-2 data. The sugarcane classification can further be used to find dense and sparse vegetation after the classification is done with deep learning models. The outcomes of this study will help to improve sugarcane categorization techniques and will help farmers, researchers, and agricultural stakeholders make better crop management, yield estimation, and resource optimization decisions in sugarcane farming.