The optimization path of agricultural industry structure and intelligent transformation by deep learning

Description

Pan and Chen build a deep learning system meant to guide farming regions toward better industry structure and smarter production. The system combines several network types: convolutional networks for images, recurrent networks for sequences, and generative networks for synthetic data. A hybrid search method blending genetic algorithms with swarm optimization tunes the models. The authors test the setup on tasks such as predicting crop yields and spotting pests and disease.

In their experiments, the method beat conventional algorithms on speed, stability, and accuracy. Yield forecasts reached the highest fit score among the models tried, and pest detection scored above 96 percent on accuracy, precision, and recall alike. The authors also report gains in disease recognition, climate prediction, and synthetic data quality. They present the system as a decision tool for planners of smart agriculture. The results come from the team's own benchmarks, so outside checks would strengthen the claims.

Catalog record

Source: https://europepmc.org/articles/PMC11603056