EcoAgriAqua AI: An Intelligent Environmental Monitoring and Decision Support System for Sustainable Agriculture and Aquaculture

Abstract

In agricultural and aquacultural ecosystems, the quick development of IoT devices, satellite imaging, and machine learning algorithms has produced previously unheard-of possibilities for smart environmental monitoring. EcoAgriAqua AI is an integrated decision support system that combines deep learning inference pipelines with multi source environmental data, such as soil physicochemical parameters, atmospheric variables, water quality indices, and crop spectral health signatures, to provide real-time, practical suggestions for sustainable farm management. A curated dataset of 54,800 annotated sensor-event examples across five environmental monitoring categories was used to train the hybrid CNN-LSTM architecture used in the suggested system. With a classification accuracy of 96.4%, precision of 95.8%, recall of 95.2%, and F1-score of 95.5%, EcoAgriAqua AI outperforms baseline models such as CNN (86.1%), Random Forest (81.1%), SVM (82.7%), and KNN (76.7%). Over a 12-month period, field testing conducted on 18 farms and 6 aquaculture ponds revealed a 31.4% decrease in water use, a 28.7% decrease in pesticide inputs, and a 22.3% increase in crop output. For precision agriculture and smart aquaculture monitoring in resource constrained developing-world environments, EcoAgriAqua AI offers a scalable, low-latency, and financially feasible solution.