用机器学习预测智能水培农场罗勒产量，兼顾精度与效率。
Machine Learning-Based Basil Yield Prediction in IoT-Enabled Indoor Vertical Hydroponic Farms
- 结合物联网传感器数据，用三种模型预测罗勒产量。
- DNN模型准确率达98%，计算速度与资源占用平衡最佳。
- 适合在边缘设备部署，为农业智能化提供决策支持。
面对水资源紧缺的挑战，尤其在突尼斯等地区，亟需高效节水的创新农业解决方案。本研究将室内垂直水培系统与机器学习（ML）技术结合，优化罗勒产量并节约用水。通过收集21株罗勒在Raspberry Pi与Arduino支持下的物联网传感器数据（包括CO2、光照等环境参数），共获取10,000个数据点，训练并评估了线性回归（LR）、长短期记忆网络（LSTM）和深度神经网络（DNN）三种模型。结果表明，尽管LSTM预测准确率高达99%，但其执行时间是LR的10倍，内存占用约为DNN的3倍；而DNN模型达到98%准确率，兼具较快速度与较低资源消耗，适用于实际部署；LR仅需11秒完成预测，适合低复杂度或资源受限场景。该研究揭示了基于DNN的方案在农业环境中构建高响应、高精度决策支持系统的潜力，具备边缘设备部署前景。
原文摘要 · Abstract (English)
As agriculture faces increasing pressure from water scarcity, especially in regions like Tunisia, innovative, resource-efficient solutions are urgently needed. This work explores the integration of indoor vertical hydroponics with Machine Learning (ML) techniques to optimize basil yield while saving water. This research develops a prediction system that uses different ML models and assesses their performance. The models were systematically trained and tested using data collected from IoT sensors of various environmental parameters like CO2, light. The experimental setup features 21 basil crops and uses Raspberry Pi and Arduino. 10k data points were collected and used to train and evaluate three ML models: Linear Regression (LR), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN). The comparative analysis of the performance of each model revealed that, while LSTM showed high predictive capability and accuracy of 99%, its execution time was 10 times longer than LR and its RAM usage was about 3 times higher than DNN's when simulated on a standard CPU environment. Conversely, the DNN model had an accuracy rate of 98%. This proves an efficient balance between computational speed and prediction quality, which makes this model well-suited for real-life deployment. Moreover, LR excelled in fast processing of basic prediction with an execution time of 11 seconds. This makes the LR model more suitable for low-complexity or resource-limited applications. These performance trade-offs highlight the potential of DNN-based solutions for building responsive, high-accuracy decision-support systems tailored to agricultural environments, making it suitable for future edge-device deployment.
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