Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks
Wei Xu
Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks - Netherlands : Springer, 2018.
Peer review
As crop diseases bring huge losses every year in both developed and developing countries, determining how to precisely predict crop disease severity to facilitate agricultural emergency management is really a worldwide problem. Previous studies have introduced machine learning (ML) techniques into crop disease prediction and achieved better experimental results. However, the architectures of these ML models are unsuitable to model time series data. Moreover, the dependences among observations over time and across space have not been taken into account in model construction. By applying data-mining techniques to dynamic spatial panels of remote sensing data and considering features of bioclimatic, topographic and soil conditions as a supplement, we propose a novel crop disease prediction framework for agricultural emergency management based on ensemble learning techniques and spatio-temporal recurrent neural network (STRNN) which is an extension of recurrent neural network (RNN) in time and space. Empirical experiments are conducted on a specific dataset which is built based on reported cases of wheat yellow rust outbreaks in the Longnan city. Experimental results indicate that our proposed method outperforms all baseline models in crop disease severity prediction. The managerial implication of our work is that by applying the proposed methodology, some preparedness measures can be implemented in advance to prevent or mitigate the possible disasters according to predicted results. Notable economic and ecological benefits can be achieved by optimizing the frequency and timing of application of fungicide, pesticides and other preventative measures.
Text in English
1384-6175 1573-7624 (Online)
https://doi.org/10.1007/s10707-017-0314-1
Crops
Plant diseases
Artificial intelligence
Learning
Neural Networks
Emergencies
Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks - Netherlands : Springer, 2018.
Peer review
As crop diseases bring huge losses every year in both developed and developing countries, determining how to precisely predict crop disease severity to facilitate agricultural emergency management is really a worldwide problem. Previous studies have introduced machine learning (ML) techniques into crop disease prediction and achieved better experimental results. However, the architectures of these ML models are unsuitable to model time series data. Moreover, the dependences among observations over time and across space have not been taken into account in model construction. By applying data-mining techniques to dynamic spatial panels of remote sensing data and considering features of bioclimatic, topographic and soil conditions as a supplement, we propose a novel crop disease prediction framework for agricultural emergency management based on ensemble learning techniques and spatio-temporal recurrent neural network (STRNN) which is an extension of recurrent neural network (RNN) in time and space. Empirical experiments are conducted on a specific dataset which is built based on reported cases of wheat yellow rust outbreaks in the Longnan city. Experimental results indicate that our proposed method outperforms all baseline models in crop disease severity prediction. The managerial implication of our work is that by applying the proposed methodology, some preparedness measures can be implemented in advance to prevent or mitigate the possible disasters according to predicted results. Notable economic and ecological benefits can be achieved by optimizing the frequency and timing of application of fungicide, pesticides and other preventative measures.
Text in English
1384-6175 1573-7624 (Online)
https://doi.org/10.1007/s10707-017-0314-1
Crops
Plant diseases
Artificial intelligence
Learning
Neural Networks
Emergencies