Modelling the Canopy Conductance of Cocoa Tree Using a Recurrent Neural Network

Sajo, Opeyemi Samuel; Oguntunde, Philip G.; Fasinmirin, Johnson Toyin; Akinnagbe, Akindele; Olufayo, Ayorinde A.; Agele, Samuel Ohikhena

doi:10.11648/j.ajnna.20210702.11

Cited by 1 publication

(1 citation statement)

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“…Recurrent neural networks (RNN) are designed to deal with serial dependencies [50], so they can extract the temporal information in the data. In time series problems, RNN is commonly used [51].…”

Section: Ai Algorithmsmentioning

confidence: 99%

Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of Magnolia denudata in Shanghai Urban Green Spaces

Zhang,

Feng

et al. 2023

Forests

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The measurement of plant sap flow has long been a traditional method for quantifying transpiration. However, conventional direct measurement methods are often costly and complex, thereby limiting the widespread application of tree sap flow monitoring techniques. The concept of a Virtual Measurement Instrument (VMI) has emerged in response to this challenge by combining simple instruments with Artificial Intelligence (AI) algorithms to indirectly assess specific measurement objects. This study proposes a tree sap flow estimation method based on environmental factors and AI algorithms. Through the acquisition of environmental factor data and the integration of AI algorithms, we successfully achieved indirect measurement of tree sap flow. Accounting for the time lag response of the flow to environmental factors, we constructed the Magnolia denudata sap flow estimation model using the K-Nearest Neighbor (KNN), Random Forest (RF), Backpropagation Neural Network (BPNN), and Long Short-Term Memory network (LSTM) algorithms. The research results showed that the LSTM model demonstrated greater reliability in predicting sap flow velocity, with R2 of 0.957, MAE of 0.189, MSE of 0.059, and RMSE of 0.243. The validation of the target tree yielded an R2 of 0.821 and an error rate of only 4.89% when applying the model. In summary, this sap flow estimation method based on environmental factors and AI provides new insights and has practical value in the field of tree sap flow monitoring.

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Section: Ai Algorithmsmentioning

confidence: 99%