Research on Water-Fertilizer Integrated Technology Based On Neural Network Prediction and Fuzzy Control

Sun, Fengshen; Ma, Weishun; Li, Heju; Wang, Songhong

doi:10.1088/1755-1315/170/3/032168

Cited by 14 publications

(6 citation statements)

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“…Sistem irigasi terpadu telah berhasil diimplementasikan di berbagai belahan dunia, termasuk Afrika Selatan (Van Koppen et al, 2020), dan Iran (Plusquellec, 1996). Sistem ini memiliki potensi untuk mengubah pertanian padi tadah hujan tradisional menjadi sistem yang lebih berkelanjutan dan produktif.…”

Section: Pendahuluanunclassified

“…Beberapa penelitian menunjukkan bahwa ada berbagai teknologi yang digunakan dalam sistem irigasi, termasuk pertanian presisi, sistem irigasi pintar, dan teknologi terintegrasi air-pupuk. Teknologi-teknologi ini melibatkan penggunaan sensor untuk memantau parameter tanah dan cuaca, yang membantu mengurangi pemborosan air dan listrik (Adetiba et al, 2022;Aziz et al, 2021;Işık et al, 2017;Sun et al, 2018). Sistem otomatis juga diusulkan untuk secara efektif membantu petani di daerah pedesaan memasok air ke lahan pertanian mereka, meningkatkan produktivitas, dan memberikan kontrol kualitas yang lebih baik, serta meminimalkan waktu kerja tenaga kerja (Aziz et al, 2021).…”

Section: Sistem Irigasi Terpaduunclassified

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Penerapan Irigasi Terpadu untuk Mengatasi Musim Kemarau dalam Pertanian Padi

Harahap,

Pahrijal

2023

JGWS

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Pertanian padi merupakan kegiatan pertanian yang vital di Indonesia, dengan Jawa Barat sebagai wilayah yang sangat penting dalam lanskap pertanian ini. Namun, musim kemarau yang berulang menimbulkan tantangan yang signifikan terhadap budidaya padi di wilayah ini. Sistem irigasi terpadu, yang menggabungkan berbagai sumber air dan teknik irigasi yang canggih, menawarkan solusi potensial untuk mengatasi tantangan ini. Penelitian ini menggunakan pendekatan metode campuran, yang mengintegrasikan pengumpulan dan analisis data kuantitatif dan kualitatif, untuk menilai secara komprehensif implementasi sistem irigasi terpadu di Jawa Barat. Temuan-temuannya menunjukkan distribusi sistem irigasi terpadu yang tidak merata di wilayah studi, dengan tingkat adopsi yang lebih tinggi di beberapa daerah dibandingkan dengan daerah lainnya. Air permukaan merupakan sumber utama untuk sistem ini, yang mendukung keandalan sumber air di daerah-daerah tertentu. Penelitian ini menunjukkan bahwa sistem irigasi terpadu secara signifikan meningkatkan hasil panen padi, dengan peningkatan rata-rata 20% selama musim kemarau. Selain itu, petani yang mengadopsi sistem ini melaporkan pendapatan bersih yang lebih tinggi, yang menekankan kelayakan ekonomi dari teknologi tersebut. Tantangan yang dihadapi antara lain biaya awal yang tinggi, kebutuhan akan pengetahuan teknis, dan masalah hak atas air, yang menghambat adopsi sistem irigasi terpadu secara luas. Namun, manfaatnya sangat besar, termasuk peningkatan produktivitas, pengurangan kerentanan terhadap variabilitas iklim, pemberdayaan ekonomi, dan ketahanan iklim. Selain itu, studi ini juga menyoroti aspek sosial-budaya dalam adopsi, menggarisbawahi pentingnya keterlibatan masyarakat dan pengetahuan bersama.

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Section: Pendahuluanunclassified

Section: Sistem Irigasi Terpaduunclassified

Penerapan Irigasi Terpadu untuk Mengatasi Musim Kemarau dalam Pertanian Padi

Harahap,

Pahrijal

2023

JGWS

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“…To further improve the performance of ANN for irrigation water management, the integration of fuzzy logic was used to compensate for the performance of the neural network, through the fusing of different parameters from various sensors used for sensing the irrigation environment [91]. A similar method was investigated using ANN and a fuzzy controller for water and fertilizer saving [92]. The monitoring of the volumetric water content of the soil is required for irrigation scheduling and water resource allocation, management and planning.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…Gómez-Melendez et al [11] used a fuzzy control algorithm to achieve pH adjustment of a water-fertilizer integrated control system, but the system can only reflect the pH of the water-fertilizer solution, which cannot reflect the amount of each fertilizer applied. Sun F S et al [12] proposed time-varying nonlinear control algorithms to regulate the concentration of water-fertilizer and achieved a high control accuracy.…”

Section: Introductionmentioning

confidence: 99%

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Guo,

Zhu,

Zhao

et al. 2024

Processes

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Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

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Research on Water-Fertilizer Integrated Technology Based On Neural Network Prediction and Fuzzy Control

Cited by 14 publications

References 0 publications

Penerapan Irigasi Terpadu untuk Mengatasi Musim Kemarau dalam Pertanian Padi

Penerapan Irigasi Terpadu untuk Mengatasi Musim Kemarau dalam Pertanian Padi

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

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