Special Issue "New Perspectives in Agricultural Water Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Long Wang
E-Mail Website
Guest Editor
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Interests: machine learning; data mining; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Huang
E-Mail Website
Guest Editor
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China
Interests: machine learning; computational intelligence; renewable energy systems; complex systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenhua Wang
E-Mail Website
Guest Editor
College of Water Resources and Architectural Engineering, Shihezi University, Shihezi, China
Interests: saline water irrigation; microirrigation; agricultural water management

Special Issue Information

Dear Colleagues,

Advances in information and communication technologies have driven the evolution of traditional agricultural production into smart farming. Since irrigation and drainage scheduling significantly impacts crop production, agricultural water management is crucial in modern agriculture. Nowadays, thanks to the Internet of Things (IoT) techniques various types of connected sensors are being installed in farms. For instance, besides traditional soil moisture sensors, digital cameras now can be employed to monitor plant growth in a real-time manner. Therefore, more data and information related to every aspect of agricultural production is collected and agricultural water management is becoming complicated and complex by considering all the available information. The accumulated data from different sensors covers a wide range of data formats, such as time-series, images, videos, sound waves, etc. To achieve better decision-making in agricultural water management, intelligent information processing and data analytics approaches are highly desired and thus both structured and unstructured data can be fully utilized.

This special issue will focus on new perspectives in agricultural water management driven by the IoT and sensor techniques. The goal is that it provides an opportunity for us to gain a significantly better understanding of the current developments and the future direction of smart agricultural water management.

Potential topics include but are not limited to the following:

  • IoT solutions for Agricultural Water Management
  • Data-driven Soil Moisture Forecasting Approaches
  • Image Processing and Computer Vision Algorithms for Plant Water Analysis
  • Metaheuristic Algorithms for Irrigation and Drainage Scheduling
  • Multi-Source Data Fusion Algorithms
  • Machine Learning and Deep Learning Algorithms for Irrigation System Modelling

Dr. Long Wang
Dr. Chao Huang
Prof. Dr. Zhenhua Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • irrigation
  • drainage
  • machine learning
  • internet of things
  • sensor technologies
  • data-driven

Published Papers (3 papers)

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Research

Article
Effects of Soil Texture on Soil Leaching and Cotton (Gossypium hirsutum L.) Growth under Combined Irrigation and Drainage
Water 2021, 13(24), 3614; https://doi.org/10.3390/w13243614 - 16 Dec 2021
Viewed by 428
Abstract
To further explore the effects of different soil textures on soil leaching and cotton (Gossypium hirsutum L.) growth using a combined irrigation and drainage technique and provide a theoretical basis for the improvement of saline alkali land in Xinjiang, we used a [...] Read more.
To further explore the effects of different soil textures on soil leaching and cotton (Gossypium hirsutum L.) growth using a combined irrigation and drainage technique and provide a theoretical basis for the improvement of saline alkali land in Xinjiang, we used a test pit experiment to test soil moisture, salinity, soil pH, permeability, cotton agronomic characteristics, cotton yield and quality, and water use efficiency in three soil textures (clay, loam, sand soil) under the combined irrigation and drainage (T1) and conventional drip irrigation (T2). We measured the soil moisture content in different soil layers of clay, loam and sandy soil under the T1 and T2 treatments. Clay and loam had better water retention than sandy soil, and the soil moisture under the combined irrigation and drainage treatment was slightly higher than that under conventional drip irrigation. Under T1, the average salt content and pH value in the 0–60 cm soil layer of clay, loam and sandy soil decreased by 14.09%, 14.21% and 12.35%, and 5.02%, 5.85% and 3.27%, respectively, compared with T2. Therefore, T2 reduced the salt content and pH value of shallow soil. Under T1 and T2, the relative permeability coefficient (K/K0) values in different soil textures at different growth stages of cotton were ranked as follows: sandy soil > loam > clay. Under T1, the K/K0 values for different soil textures at different growth stages of cotton were >1; therefore, T1 improved soil permeability. The yield and water use efficiency of seed cotton under T1 and T2 in different soil textures were ranked as follows: loam > clay > sand, and there were significant differences between the different treatments. In loam, the cotton yield and water use efficiency of the combined irrigation and drainage treatment were 6.37% and 13.70% higher than those for conventional drip irrigation treatment, respectively. By combining irrigation and drainage to adjust the soil moisture, salt, pH value and soil permeability of different soil textures, the root growth environment of crops can effectively be improved, which is of great significance to improving the utilization efficiency of water and fertilizer and promoting the growth of cotton. Full article
(This article belongs to the Special Issue New Perspectives in Agricultural Water Management)
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Article
A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture
Water 2021, 13(20), 2907; https://doi.org/10.3390/w13202907 - 16 Oct 2021
Viewed by 477
Abstract
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new [...] Read more.
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively. Full article
(This article belongs to the Special Issue New Perspectives in Agricultural Water Management)
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Article
3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting
Water 2021, 13(13), 1773; https://doi.org/10.3390/w13131773 - 27 Jun 2021
Viewed by 588
Abstract
The instability and variability of solar irradiance induces great challenges for the management of photovoltaic water pumping systems. Accurate global horizontal irradiance (GHI) forecasting is a promising technique to solve this problem. To improve short-term GHI forecasting accuracy, ground-based sky image is valuable [...] Read more.
The instability and variability of solar irradiance induces great challenges for the management of photovoltaic water pumping systems. Accurate global horizontal irradiance (GHI) forecasting is a promising technique to solve this problem. To improve short-term GHI forecasting accuracy, ground-based sky image is valuable due to its correlation with solar generation. In previous studies, great efforts have been made to extract numerical features from sky image for data-driven solar irradiance forecasting methods, e.g., based on pixel-value color information, and based on the cloud motion detection method. In this work, we propose a novel feature extracting method for GHI forecasting that a three-dimensional (3D) convolutional neural network (CNN) is developed to extract features from sky images with efficient training strategies. Popular machine learning algorithms are introduced as GHI forecasting models and corresponding forecasting accuracy is fully explored with different input features on a large dataset. The numerical experiment illustrates that the minimum average root mean square error (RMSE) of 62 W/m2 is achieved by the proposed method with 15.2% improvement in Skill score against baseline forecasting method. Full article
(This article belongs to the Special Issue New Perspectives in Agricultural Water Management)
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