Pomegranate Farming Monitoring System

Abstract

Pomegranate farming's enhanced predictive accuracy for nutrient deficiency and fertilizer recommendation was achieved through systematic evaluations of various machine learning algorithms on a comprehensive dataset. Remarkably, the Random Forest algorithm demonstrated exceptional accuracy of 99.85%, reaffirming its potential for accurate predictions in NPK value-based deficiency and disease outbreak prediction. In the context of climate-driven pest and disease outbreak forecasts, a meticulous exploration of machine-learning algorithms was conducted. Among these, the Random Forest Classifier stood out with an accuracy of 99.00%, aligning with the study's accuracy-driven focus and highlighting its capability to address climate-induced pest and disease dynamics. For "Pomegranate Disease Detection" using Convolutional Neural Networks (CNN), the VGG16 architecture yielded an accuracy of 98.44%, showcasing the power of automated disease identification in advancing agricultural practices. In "Pomegranate Quality Detection and Analysis," Convolutional Neural Networks (CNN) with VGG16 achieved an impressive 97% accuracy, demonstrating its potential for enhancing fruit quality assessment in pomegranate cultivation. These findings collectively underscore the significance of advanced algorithms in optimizing various facets of pomegranate farming, from nutrient management to disease detection and quality assessment.