Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Abstract

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

“…The simulation results show that the proposed algorithms are effective. The proposed methods in this article can combine some mathematical strategies [63][64][65][66] and can be extended to study the filtering, estimation, and prediction problems of different engineering systems with colored noises [67][68][69][70][71][72] and can be applied to other literature studies [73][74][75][76] such as information processing and networked communication systems.…”

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

“…In this experiment, 6 models are used for comparison with the proposed method, which are RNN [49], LSTM [51], GRU [53], EMDCNN_RNN [49] and EMDCNN_LSTM [51] (which are obtained by decomposing the data using EMD and with classification of the CNN) as the sub-predictors, and finally the sequential two-level method (STL) method from [17]. The temperature, wind speed, and humidity data introduced in Section 4.1 are used to show the prediction result.…”

“…In Table 1, a comparison between the proposed method and RNN [49], LSTM [51], GRU [53], EMDCNN_RNN [49] (EMD and CNN-based RNN [49]), EMDCNN_LSTM [51] (EMD and CNN-based LSTM [51]), and STL [17] in terms of RMSE. Table 2 gives comparisons the means between the proposed method and RNN [49], LSTM [51], and GRU [53]; between the proposed method and STL, EMDCNN_RNN, and EMDCNN_LSTM; and between the proposed method and EMDCNN_RNN and EMDCNN_LSTM.…”

“…To apply the IoT technology to the development of modern agriculture, many researchers have made many outstanding contributions [15][16][17]. One example is an agricultural Internet of Things system that is used to help monitor plant production processes and enable early warning and rapid diagnosis of major pests and diseases [18].…”

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

“…Moreover, the prediction of the water quality [39] should be introduced to pre-judge the trend. The prediction models [40][41][42][43] and data estimation methods [44,45] can help data analysis in the aforehand decision-making.…”