Effective crop categorization using wavelet transform based optimized long short-term memory technique

Abstract

Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop categorization. Image denoising is performed by WT. Denoising algorithms for images attempt to find a middle ground between totally removing all of the image’s noise and preserving essential, signal-free components of the picture in their original state. After denoising of images, crop image classification is achieved by LSTM and support vector machine (SVM) algorithm. LSTM has achieved 99.5% accuracy.