Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture

Abstract

IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in agriculture.AI-enabled sensors function as smart sensors and IoT has made various types of sensor-based equipment in the field of agriculture. This research proposes novel techniques in AI technique based soft sensor integrated with remote sensing model using deep learning architectures. The input has been pre-processed to recognize the missing value, data cleaning and noise removal from the image which is collected from the agricultural land. The feature representation has been carried out usingweight-optimized neural network with maximum likelihood (WONN_ML). after representing the features, classification process has been carried out using ensemble architecture of stacked auto-encoder and kernel-based convolution network (SAE_KCN). The experimental results have been done for various crops in terms of computational time of 56%, accuracy 98%, precision of 85.5%, recall of 89.9% and F-1 score of 86% by proposed technique.