Optimization of perennial plantation management in agriculture based on neural networks

Perennial plantations are a strategic component of sustainable agriculture, contributing to long-term productivity and environmental stability. However, effective management of these crops requires adaptive approaches capable of responding to changing climatic, environmental and economic conditions. This study examines the use of neural networks to optimize the management of perennial plantations in agrarian systems. A deep learning model based on a multilayer perseptron architecture was developed and trained using regional agricultural data including climatic indicators, soil characteristics, and historical yield data. The model was applied to simulate different management scenarios and predict crop productivity under different environmental conditions. The results showed that the proposed neural network approach improved the accuracy of yield prediction by 15-20% compared to traditional methods and improved the efficiency of resource allocation. The findings demonstrate the potential of neural networks in decision support for perennial crop management and emphasize their role in the development of precision agriculture and climate-resilient agro-technologies.