The fuzzy inference system for intelligent water quality monitoring system to optimize eel fish farming

2016 ◽  
Author(s):  
Sri Wahjuni ◽  
Ardhi Maarik ◽  
Tatag Budiardi
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fish Farming ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Inference System

scholarly journals Mamdani fuzzy inference system for mapping water quality level of biofloc ponds in catfish cultivation

2020 ◽  
Vol 8 (2) ◽  
pp. 84-88
Author(s):  
Herryawan Pujiharsono ◽  
Danny Kurnianto
Keyword(s):  
Water Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Quality Monitoring ◽  
Growth Conditions ◽  
Fish Growth ◽  
Quality Level ◽  
Inference System ◽  
The Government ◽  
Mamdani Fuzzy Inference System

The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.

scholarly journals Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy

2021 ◽  
Vol 8 (1) ◽  
pp. 157
Author(s):  
Fachrul Rozie ◽  
Iwan Syarif ◽  
Muhammad Udin Harun Al Rasyid ◽  
Edi Satriyanto
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Smart Phone ◽  
Fish Farming ◽  
Water Circulation ◽  
Inference System ◽  
And Control ◽  
Internet Of Things Technology

<p class="Abstrak">Akuaponik adalah penggabungan sistem budidaya akuakultur dan hidroponik yang dapat menjadi solusi untuk mengatasi keterbatasan lahan, keterbatasan sumber air serta meningkatkan ketahanan pangan. Pada sistem akuaponik, kualitas air pada budidaya ikan merupakan salah satu syarat utama dalam keberhasilan proses budidaya. Penelitian ini mengkombinasikan peternakan lele dengan penanaman kangkung hidroponik. Kotoran ikan lele dan sisa makanan terakumulasi di air dan dapat menjadi racun bagi ikan lele karena mengandung kadar anomia yang tinggi sehingga sangat berbahaya jika tidak dibuang. Air ini kemudian dialirkan ke tanaman kangkung hidroponik melalui biofilter yang bermanfaat sebagai pengurai air kotor dari kolam menjadi nitrat dan nitrit yang berguna sebagai nutrisi tanaman. Selanjutnya setelah air menjadi bersih dan mempunyai kadar oksigen yang tinggi, air tersebut dialirkan kembali ke kolam ikan lele. Penelitian ini bertujuan untuk mengembangkan sistem cerdas pada budidaya akuaponik dengan memanfaatkan teknologi <em>Internet of Things</em><em> </em>yang dilengkapi dengan beberapa jenis sensor untuk memantau dan mengendalikan kualitas air dengan menerapkan algoritma Sistem Inferensi Fuzzy /<strong><em> </em></strong><em>Fuzzy Inference System </em>(FIS) untuk mengatur kecepatan sirkulasi air kolam agar menghemat daya listrik pada pompa<em>.</em> Peralatan ini juga dilengkapi dengan layanan pemberian pakan ikan secara otomatis yang dapat diprogram sesuai kebutuhan. Sistem akuaponik ini dapat dipantau melalui web maupun ponsel pintar berbasis android. Pengujian yang dilakukan terhadap perbandingan keputusan oleh pakar dan sistem FIS pada kecepatan sirkulasi air sistem akuaponik menunjukkan nilai akurasi 83,33%, dan hasil dari pengujian ketepatan alat pemberi pakan yang dibuat secara otomatis terhadap ketepatan pemberian pakan secara manual memiliki nilai akurasi 90,97%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul21"><em><span lang="IN">Aquaponics is a combination of aquaculture and hydroponic cultivation systems that can be a solution to overcoming limited land, limited water sources and increasing food security. In the aquaponics system, water quality in fish farming is one of the main requirements in the success of the cultivation process. This research combines catfish farming with hydroponic kale cultivation. Catfish feces and food scraps accumulate in water and can be toxic to catfish because they contain high levels of anomia so it is very dangerous if not disposed of. This water is then flowed to hydroponic kale plants through a biofilter which is useful as decomposing dirty water from the pond into nitrates and nitrites which are useful as plant nutrients. Furthermore, after the water becomes clean and has high oxygen levels, the water is flowed back into the catfish pond. This study aims to develop a smart system in aquaponic cultivation by utilizing Internet of Things technology which is equipped with several types of sensors to monitor and control water quality by applying the Fuzzy Inference System (FIS) algorithm to regulate the speed of pool water circulation in order to save electric power on the pump. This equipment is also equipped with an automatic fish feeding service which can be programmed as needed. This aquaponics system can be monitored via the web or an Android-based smart phone. Tests carried out on the comparison of decisions by experts and the FIS system on the water circulation speed of the aquaponics system show an accuracy value of 83.33%, and the results of testing the accuracy of the feeder that is made automatically against the accuracy of manual feeding have an accuracy value of 90.97% .</span></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals A Review on Visual Water Quality Monitoring System in Precision Aquaculture

2019 ◽  
Vol 892 ◽  
pp. 23-30
Author(s):  
Alter Jimat Embug ◽  
Ag Asri Ag Ibrahim ◽  
Muzaffar Hamzah ◽  
Mohammad Fadhli Asli
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Current Practice ◽  
Fish Farming ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Auditory Display

This paper presents the review of available visual water quality monitoring and proposes a conceptual sonification model of audiovisual analytics for precision aquaculture. This study reviews the current practice of the visual water quality monitoring system used to interpret the complex fish farming data. This study also explores the possibility of using an auditory display, by using sound as complementary elements to communicate information from the system to the user.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Research on Fishery Water Quality Monitoring System Based on 6LoWPAN

2020 ◽  
Vol 1624 ◽  
pp. 042057
Author(s):  
Xueying Wang ◽  
Yanli Feng ◽  
Jiajun Sun ◽  
Dashe Li ◽  
Huanhai Yang
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring

IoT-based Smart Water Quality Monitoring System for Malaysia

2021 ◽  
Author(s):  
Kamalanathan Shanmugam ◽  
Muhammad Ehsan Rana ◽  
Roshenpal Singh Jaspal Singh
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Smart Water
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