Inflate Agriculture Crop Yield Prophesy through Deep Neural Network Schemes

Recent advancements in DL (Deep Learning) have unlocked the potential for more accurate predictions in various fields, including agricultural crop yield forecasting. This paper presents the NeuroDendritic Networks (NDN) methodology, a biologically inspired approach that emulates the dynamic growth and retraction of dendrites in neurons. This novel method hinges on the NeuroDendritic Units (NDUs) and their ability to grow and prune dendritic branches adaptively based on data stimuli, ensuring optimal processing and information storage. As a result, NDN offers dynamic model adaptation, intrinsic regularization through dendritic retraction, and enhanced focus on pertinent data patterns. Preliminary tests have shown an expected improvement of up to 40% in prediction accuracy compared to traditional methods. This dynamic adaptation ensures the model remains attuned to the most influential factors affecting yield, leading to more informed and timely interventions. The versatility and adaptability of the NDN method not only redefine the standards in agricultural predictive modeling but also hint at its expansive potential across various domains, heralding a promising direction for future research.