Harnessing AI for agriculture utilizing color-condition camera sensors and thermal imaging drones for crop color-condition detection and predictive yield analysis with inventory management system

Abstract

Technological Gaps and Challenges for Agriculture in the Philippines Such advancements may be relevant to other countries as well but we should also consider that there is greater potential of technology adoption with these purposes than ever befo re, especially those small-scale farmers from local areas who have less accessibility on technological trendsetting. In this study, we attempt to solve these problems using Artificial Intelligence (AI) in order to improve crop monitoring and predictive yiel d amidst the climate change. Color-condition detection using Convolutional Neural Networks (CNN) with 76.97% accuracy and predictive analysis by Artificial Neural Network(ANN). This optimizes the timing of planting and harvest, depending on the combination of these algorithms.

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