[论文解读] Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

Bashar Alhnaity, Simon Pearson+2 more|arXiv (Cornell University)|Jul 1, 2019

Greenhouse Technology and Climate Control参考文献 21被引用 91

一句话总结

本论文部署了一种基于深度循环神经网络的LSTM，用于在受控温室环境中预测番茄产量和 Ficus benjamina 茎干生长，并将其性能与 SVR 和 RF 进行比较，基于微气候数据。

ABSTRACT

Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

研究动机与目标

• 推动对植物生长和产量的准确预测，以改善环境控制并降低成本。
• 在两种温室场景下建模生长变异和产量（番茄产量预测与 Ficus benjamina 茎干生长）。
• 结合微气候数据与生长指标以提升预测准确性。

提出的方法

• 引入基于 Long Short-Term Memory (LSTM) 单元的深度循环神经网络用于时间序列预测。
• 将目标生长参数与微气候条件作为模型输入。
• 采用均方误差作为评判标准，对比评估传统机器学习方法，如支持向量回归 (SVR) 和随机森林回归 (RFR)。
• 在 EU Interreg SMARTGREEN 项目（2017-2021）下从两座温室收集的数据上验证模型。
• 在受控温室环境中，呈现针对产量和生长预测的专注、应用型评估。

实验结果

研究问题

• RQ1 基于深度 LSTM 的 RNN 能否在温室条件下利用微气候和生长数据准确预测番茄产量？
• RQ2 基于 LSTM 的模型在预测植物生长指标（如茎径和总体产量）方面能否优于 SVR 和随机森林？
• RQ3 预测在两种不同温室环境（Belgium 和 UK）中的泛化能力如何？

主要发现

• 基于 LSTM 的模型在预测产量和生长变异方面提供了非常有前景的结果。
• 研究包含对 SVR 和随机森林回归的对比分析。
• 使用来自 Belgium 和 UK 的两座温室的数据，在 SMARTGREEN 框架内评估模型。
• 同时利用生长数据和微气候输入来建模目标生长参数。
• 本文展示了深度学习在提升温室农业环境控制和生产计划方面的潜力。