Identifying peanut maturity based on Hyper Spectral Invariant Scaled Feature Selection using Adaptive Dense Net Recurrent Neural Network

Abstract

With the rapid development of agricultural resources in south India, the peanut is important for cultivation in delta regions. The findings of suitable peanut seeds are analyzed through digital image processing with the support of deep learning analysis. Identifying seed maturity is a challenging task because seed level finding in features contains various scaled our dimensions, and increasing on successive feature observation leads to maturity findings inaccuracy in precision rate. We propose a Hyper Spectral Invariant Scaled Feature Selection (HSISFS) using an Adaptive Dense Net Recurrent Neural Network (ADNRNN) to resolve this problem. This initiates with preprocessing and scaling peanut entities' regions to normalize the image pixel by reducing the noiseless image. The entries of the peanut image features are observed by covering the exact boundary regions to apply the Space Retention Log Edge Detector (SRLogED) to make sliding segmentation. The sliding segments are normalized to find the matured objects through color quantization by observing pods entities. This is enhanced with Entity scalar histogram equalization (ESHE) by projecting the entities of peanut buds regions to find the scaled weights of features. Then the equivalent distance of matured and non-matured seeds are extracted through the hyperspectral Invariant scaled feature selection method and classified using the Adaptive Dense Net Recurrent Neural Network Algorithm. This proposed system produces high classification results by finding the exact scaled feature dimension to predict the maturity of seed and higher performance to increase the precision rate compared to the other systems.