Precision irrigation, defined as an efficient water allocation technique characterized by the optimal management and best collaboration of various factors of the irrigation process, attracts considerable attention in agricultural production and crop cultivation. This paper reviews the latest research developments in water allocation mechanism and integrative management effectiveness of precision irrigation, and highlights how irrigation water allocation and integrative management contribute to the high-efficiency performance of precision irrigation techniques; the irrigation models, irrigation infrastructure, and management strategies currently being used are emphasized. Thereafter, the future development prospects in water allocation and integrative management could be systematically analyzed and subsequently explored. Some frontier techniques such as data-oriented irrigation management, performance-proven water allocation, and cloud-based irrigation control are among the critical technologies capable of building a sustainable, integrative, and evolutionary irrigation system while providing the higher quality and efficiency needed for a full application of precision irrigation. This review could be used as an effective reference to study the complicated correlations between precision irrigation and its constructive influences in different environmental conditions, and to facilitate the practical promotion of irrigation productivity with higher accuracy and increased reliability of returns.

As the high productive efficiency of sprinkler irrigation is largely based on balanced soil moisture distribution, it is essential to study the exact effectiveness of water droplet infiltration, which provides a theoretical basis for rationally scheduling the circulation efficiency of groundwater in agricultural irrigation performance. This research carried out adaptive prediction of the droplet infiltration effectiveness of sprinkler irrigation by using a novel approach of a regularized sparse autoencoder–adaptive network-based fuzzy inference system (RSAE–ANFIS), for the purpose of quantifying actual water droplet infiltration and effectiveness results of precision irrigation in various environmental conditions. The intelligent prediction experiment we implemented could be phased as: the demonstration of governing equations of droplet infiltration for sprinkler irrigation modeling; the measurement and computation of probability densities in water droplet infiltration; innovative establishment and working analysis of RSAE–ANFIS; and the adaptive prediction of infiltration effectiveness indexes, such as average soil moisture depth increment (θ, mm), irrigation infiltration efficiency (e_a, %), irrigation turn duration efficiency (e_t, mm/min), and the uniformity coefficient of soil moisture infiltration (C_u, %), which were implemented to provide a comprehensive illustration for the effective scheduling of sprinkler irrigation. Result comparisons indicated that when jetting pressure (P_w) was 255.2 kPa, the impinge angle (W_a) was 42.5°, the water flow rate (F_a) was 0.67 kg/min, and continuous irrigation time (T_c) was 32.4 min (error tolerance = ±5%, the same as follows), thereby an optimum and stable effectiveness quality of sprinkler irrigation could be achieved, whereas average soil moisture depth increment (θ) was 57.6 mm, irrigation infiltration efficiency (e_a) was 62.5%, irrigation turn duration efficiency (e_t) was 34.5 mm/min, and the uniformity coefficient of soil moisture infiltration (C_u) was 53.6%, accordingly. It could be concluded that the proposed approach of the regularized sparse autoencoder–adaptive network-based fuzzy inference system has outstanding predictive capability and possesses much better working superiority for infiltration effectiveness in accuracy and efficiency; meanwhile, a high agreement between the adaptive predicted and actual measured values of infiltration effectiveness could be obtained. This novel intelligent prediction system has been promoted constructively to improve the quality uniformity of sprinkler irrigation and, consequently, to facilitate the productive management of sprinkler irrigated agriculture.

Considering the high-quality requirements related to agricultural crop production, the collaborative operation and application influence of sprinkler drip irrigation is an important issue in precision agriculture. The objective of this review is to give a comprehensive demonstration focusing on the subject of collaborative operation and application influence of sprinkler drip irrigation, by using a set of comparative analysis and literature bibliometric maps, therefore the sprinkler drip irrigation quality considering actual influential factors could be determined and analyzed. This review establishes on a broad spectrum of agricultural drip irrigation performance, throughout its whole procedure of collaborative monitoring, irrigation scheduling, application efficiency, and environmental influence, covering such aspects as soil physicochemical quality, irrigation scheduling, water resource redistribution, crop productivity, tillage management, climate adaptation, and environmental monitoring, etc. This review indicates that, the irrigation efficiency and drip infiltration quality of soil field can be planned precisely and allocated reasonably by sprinkler drip irrigation, which has extraordinary infiltration capability and enables much better performance, than that of other ordinary irrigation approaches in accuracy, stability, regularity, and efficiency. Thereafter, the investigation on the collaborative operation and application influence of sprinkler drip irrigation can be used to ensure the infiltration uniformity of moisture distribution, and then the high-quality requirements of practical irrigation performance can be met, too. This systematic review facilitates the productive soil-moisture-environment management for precision irrigation and agricultural production. Keywords: sprinkler drip irrigation, collaborative operation, irrigation scheduling, application efficiency, environmental influence, review DOI: 10.25165/j.ijabe.20231605.7198 Citation: Liang Z W, Zou T, Liu X C, Liu G Y, Liu Z. The collaborative operation and application influence of sprinkler drip irrigation: a systematic progress review. Int J Agric & Biol Eng, 2023; 16(5): 12–27.

The application rate for sprinkler irrigation of water–fertilizer integration machines is an important technical parameter for efficient operation. If the value is too large, the equipment will produce runoff; if it is too small, the equipment will run too long and waste energy. Therefore, it is necessary to provide a feasible scientific and theoretical basis for developing a reasonable application rate. In this study, a mathematical model of soil infiltration for sprinkler irrigation with water and fertilizer integration machines was developed. Soil water accumulation time for different soil’s initial water content, bulk density, sprinkler application rate and soil texture were derived by the finite element method, and these data were used as a training database for the neural network. To make the neural network convenient for predicting the optimal application rate of sprinkler irrigation (the maximum application rate of sprinkler irrigation without runoff) in practice, the time of waterlogging, was multiplied by the optimal application rate of sprinkler irrigation to obtain the total irrigation volume. The optimal application rate of the sprinkler irrigation prediction model of radial basis function (RBF) neural network was constructed with total irrigation water, soil bulk density, initial water content and soil texture as inputs and compared with BP neural network and generalized regression neural network. The highest prediction accuracy of RBF neural network was obtained, and its average relative error was 0.11. To verify the accuracy of the RBF neural network application rate of sprinkler irrigation prediction model in real life, a sprinkler experiment was conducted in the laboratory of Guangzhou University, and the collected soil and lawn of Guangzhou University were used to simulate the actual environment. The results showed that the relative error between the RBF neural network prediction results and the actual values was generally around 10%, while for a total irrigation volume of 58 mm, the optimal application rate of sprinkler irrigation calculated with the model was 42 (mm/h), which can save 70% of irrigation time compared to the case of using the stable infiltration rate of soil as the application rate of sprinkler irrigation without water and fertilizer. Water and fertilizer losses were not observed. This indicates that the model proposed in this study is of practical value in determining the optimum application rate of sprinkler irrigation for water–fertilizer integration machines.