Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

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Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit, and dual-rotary-encoder data are fused with Extended Kalman filter (EKF). To optimize the EKF noise matrix, the autoencoder and radial basis function (ARBF) neural network was used for modeling the state equation noise and EKF measurement equation. A multisensor AMR test platform was constructed for static experiments to estimate the circular error probability and twice-the-distance root-mean-squared criteria. Dynamic experiments were conducted on road, grass, and field environments. To validate the robustness of the proposed algorithm, abnormal working conditions of the sensors were tested on the road. The results showed that the positioning estimation accuracy was improved compared to the RTK-GNSS in all three environments. When the RTK-GNSS signal experienced interference or rotary encoders failed, the system could still improve the position estimation accuracy. The proposed system and optimization algorithm are thus significant for improving AMR position prediction performance.

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Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder Neural Network

IoT based smart agriculture systems are important for efficient usage of lands, water, and energy resources. Wireless communication protocols constitute a critical part of smart agriculture systems because the fields, in general, cover a large area requiring system components to be placed at distant locations. There are various communication protocols with different features that can be utilized in smart agriculture applications. When designing a smart agriculture system, it is required to carefully consider the features of possible protocols to make a suitable and optimal selection. Therefore, this review paper aims to underline the specifications of the wireless communication protocols that are widely used in smart agriculture applications. Furthermore, application-specific requirements, which may be useful during the design stage of the smart agriculture systems, are highlighted. To accomplish these aims, this paper compares the technical properties and investigates the practical applications of five different wireless communication protocols that are commonly used in IoT applications: ZigBee, Wi-Fi, Sigfox, NB-IoT, and LoRaWAN. In particular, the inconsistencies in the technical properties of these protocols reported in different resources have been highlighted and the reason for this situation has been discussed. Considering the features offered by the protocols and the requirements of smart agriculture applications, the appropriateness of a particular protocol to a particular smart agriculture application is examined. In addition, issues about cost, communication quality, and hardware of the five protocols have been mentioned. The trending technologies with high potential for the future applications of smart agriculture have been introduced. In this context, the relation of the technologies like aerial systems, cellular communication, and big data analytics with wireless have been specified. Finally, the leading protocol and the smart agriculture application area have been highlighted through observing the year-based distribution of the recent publications. It has been shown that usage of LoRaWAN protocol has become more widespread in recent years. 

Wireless communication protocols in smart agriculture: A review on applications, challenges and future trends

Water status and thermal response of lime trees to irrigation and shade screen

Smart fuzzy irrigation system for litchi orchards

In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

Water stress is a significant element impacting photosynthesis, which is one of the major physiological activities governing crop growth and development. In this study, the photosynthetic rate of Brassica chinensis L. var. parachinensis (Bailey) (referred to as Chinese Brassica hereafter) was predicted using the deep learning method. Five sets of Chinese Brassica were created, each with a different water stress gradient. Air temperature (Ta), relative humidity (RH), canopy temperature (Tc), transpiration rate (Tr), photosynthetic rate (Pn), and photosynthetically available radiation (PAR) were measured in different growth stages. The upper limit and lower limit equations were built using the non-water-stress baseline (NWSB) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) methods. The crop water stress index (CWSI) was then calculated using these built equations. The multivariate long short-term memory (MLSTM) model was proposed to predict Pn based on CWSI and other parameters. At the same time, the support vector regression (SVR) method was applied to provide a comparison to the MSLTM model. The results show that water stress had an important effect on the growth of Chinese Brassica. The more serious the water stress, the lower the growth range (GR). The HDBSCAN method had a lower root mean square error (RMSE) in calculating CWSI. Furthermore, the CWSI had a significant effect on predicting Pn. The regression fitting between measured Pn and predicted Pn showed that the determination coefficient (R2) and RMSE were 0.899 and 0.108 μmol·m−2·s−1, respectively. In this study, we successfully developed a method for the reliable prediction of Pn in Chinese Brassica, which can serve as a useful reference for application in water saving.

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

The invention discloses a system and method for measuring water infiltration in soil of a citrus orchard. The system comprises measuring equipment, a control device, and an executing device, wherein the measuring equipment refers to a plurality of soil moisture sensors which are arranged in a soil area to be measured according to a preset rule; the control device comprises a microcontroller, a signal conditioning module and a display module; the executing device is electromagnetic valve irrigation equipment; the soil moisture sensors are connected with the signal conditioning module through serial ports; the signal conditioning module is connected with the microcontroller through a serial port; and the microcontroller is in control connection with the display module and the electromagneticvalve irrigation equipment through serial ports respectively. The system is simple in arrangement; the microcontroller performs relevant calculation according to a relevant instruction and acquired measurement data to obtain the soil water infiltration rate; the soil water infiltration rate is displayed through the display module; simple and rapid measurement of the soil water infiltration rate is realized; and guidance is provided for citrus irrigation operation. The system and method have wide application prospects.

System and method for measuring water infiltration in soil of citrus orchard

The invention discloses a soil water content prediction system and method based on a time sequence. The system comprises a remote server, a data transmission gateway, a meteorological station in a preset range and a plurality of soil information acquisition nodes arranged in the preset range. The remote server calculates and outputs a soil water content prediction value in a preset range in the future preset duration according to soil related information acquired by soil information acquisition nodes at the same moment and the weather related information acquired by a meteorological station.The system is high in response speed and convenient, flexible, reliable and efficient to arrange; according to soil characteristics and weather conditions of different regions, soil water content prediction related parameters can be adaptively adjusted, analysis processing is performed according to atmospheric environment data and soil environment data, and a prediction value of the soil water content is given and used for guiding irrigation operation, so that the irrigation efficiency is effectively improved, and water resource waste is reduced.

Ping Zhou

Papers

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

Predicting the Photosynthetic Rate of Chinese Brassica Using Deep Learning Methods

System and method for measuring water infiltration in soil of citrus orchard

Soil water content prediction system and method based on time sequence