Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

Autor: Liyun Gong, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, Simon Pearson
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Sensors, Vol 21, Iss 13, p 4537 (2021)
Druh dokumentu: article
ISSN: 21134537
1424-8220
DOI: 10.3390/s21134537
Popis: Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Databáze: Directory of Open Access Journals
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