Simulation parameters.

The integration of IoT (Internet of Things) devices has emerged as a technical cornerstone in the landscape of modern agriculture, revolutionising the way farming practises are viewed and managed. Smart farming, enabled by interconnected sensors and technologies, has surpassed traditional methods, giving farmers real-time, granular information into their farms. These Internet of Things devices are responsible for collecting and sending greenhouse data (temperature, humidity, and soil moisture) for the required destination, to provide a comprehensive awareness of environmental factors critical to crop growth. Therefore, ensuring that the received data are accurate is a challenge, thus this paper investigates the optimization of Agriculture IoT communication, proposing a complete strategy for improving data transmission efficiency within smart farming ecosystems. The proposed model intends to maximize energy efficiency and data throughput in the context of essential agricultural factors by using Lagrange optimization and a Deep Convolutional Neural Network (DCNN). The paper focus on the ideal communication required distance between IoT sensors that measure humidity, temperature, and water levels and central control systems. The investigation emphasizes the critical necessity of these data points in guaranteeing crop health and vitality. The proposed technique strives to improve the performance of agricultural IoT communication networks through the integration of mathematical optimization and cutting-edge deep learning. This paradigm change emphasizes the inherent link between precise achievable data rate and energy efficiency, resulting in resilient agricultural ecosystems capable of adjusting to dynamic environmental conditions for optimal crop output and health.物联网（Internet of Things, IoT）设备的集成已成为现代农业发展的技术基石，彻底革新了农业生产实践的认知与管理模式。依托互联传感器与各类技术构建的智慧农业，已超越传统农业模式，为农户提供了农场场景下的实时、精细化信息。此类物联网设备负责采集并向指定终端传输温室环境数据（温度、湿度与土壤含水率），以此全面掌握对作物生长至关重要的环境参数。因此，保障接收数据的准确性成为一项核心挑战，为此本研究聚焦农业物联网通信优化问题，提出了一套可提升智慧农业生态系统内数据传输效率的完整方案。本研究所提出的模型，借助拉格朗日优化与深度卷积神经网络（Deep Convolutional Neural Network, DCNN），旨在针对核心农业生产要素最大化能源利用效率与数据吞吐量。本研究重点探讨了湿度、温度与水位监测物联网传感器同中央控制系统之间的最优通信距离。本次研究着重强调了上述数据参数对保障作物健康与生长活力的关键作用。所提技术方案通过融合数学优化与前沿深度学习技术，致力于提升农业物联网通信网络的整体性能。这一范式变革揭示了精准可达数据速率与能源效率之间的内在关联，最终构建出可适配动态环境变化的韧性农业生态系统，以实现作物产量与健康状态的最优平衡。