Intelligent Systems for Testing the Compost of Multitype Organic Matter Using Different Type Models

This research investigates the revolutionary potential of intelligent systems in the estimation of compost quality in an attempt to overcome the drawbacks of traditional methods of evaluating compost and to take advantage of its potentially game-changing potential. Because they employ a variety of machine learning models, these systems enhance efficiency, accuracy, and scalability, making them vital for sustainable waste management and agriculture. Extensive data collection, feature engineering, model training, and evaluation are all a part of the study project, which gives light on the effectiveness of these intelligent systems. Even while adaptability and precision are obvious benefits, real-world applications need to place a greater emphasis on interpretability and the integration of real-time monitoring. This work contributes to the advancement of responsible resource use, as well as sustainability and resource conservation.