Analysis of water and sediment quality in Pacific white leg shrimp Litopenaeus vannamei culture with different sediment redox potential

ABSTRAK   Pengamatan parameter kualitas air, tanah, kesehatan dan pertumbuhan merupakan hal yang penting untuk dilakukan dalam budidaya udang vaname. Keterkaitan antar parameter tersebut perlu untuk diketahui dan dianalisis lebih lanjut. Penelitian ini bertujuan untuk melihat keterkaitan antara berbagai parameter kualitas air, kualitas tanah, koefisien teknis budidaya dan parameter kesehatan udang dalam kegiatan budidaya udang dengan nilai sedimen redok potensial yang berbeda. Penelitian ini dilakukan dengan perbedaan nilai sedimen redok potensial yaitu 69,33 ± 14,5 mV, 151,00 ± 8,89 mV, dan 210,00 ± 17,32 mV dengan empat ulangan. Udang sebanyak 25 ekor dengan bobot rata rata 1,37 ± 0,04 g dan padat tebar 144 ekor/m2 digunakan dalam penelitian selama 30 hari. Selama penelitian parameter yang diamati meliputi parameter kualitas air, kesehatan dan pertumbuhan. Parameter kualitas air tidak menunjukkan perbedaaan nyata antar perlakuan kecuali nilai pH dan oksigen terlarut. Pada nilai redok potensial yang rendah nilai pH air cenderung lebih tinggi sedangkan pada nilai redok potensial yang lebih tinggi nilai oksigen terlarut lebih tinggi. Parameter kualtias air yang menunjukkan keterkaitan berdasarkan analisis komponen utama (PCA) dan klaster adalah konduktivitas dan TDS sedangkan parameter kualitas tanah yang saling berkaitan adalah total P, total Fe, total Mn dan total S. Adapun parameter pertumbuhan dan kesehatan udang tidak memenuhi syarat untuk diuji lanjut dengan PCA.   Kata kunci: kualitas air, PCA, redok potensial, sedimen, udang putih

Optimasi Tingkat Hidup Udang Crystal Red dengan Menerapkan Metode Fuzzy Logic Berbasis IOT

Banyak orang yang telah mencoba untuk membudidayakan Crystal Red Shrimp, tetapi banyak yang mengalami kegagalan akibat dari kematian masal udang. Hal tersebut seringkali terjadi karena buruknya kualitas air budidaya dan rendahnya kemampuan adaptasi lingkungan dari udang tersebut. Sangat penting untuk melakukan pengembangan sebuah sistem untuk mengatasi permasalahan tersebut, khususnya pada pemantauan dan pengendalian kualitas air. Penelitian ini membuat sebuah sistem pemantauan dan pengendalian otomatis kualitas air menggunakan Fuzzy Inference System berbasis Internet of Things. Sistem ini menggunakan beberapa sensor antara lain suhu ruangan, suhu air, Dissolved Oxygen, dan turbidity. Sensor-sensor tersebut terhubung pada Raspberry pi 3 yang telah ditanam sebuah sistem menggunakan Fuzzy Inference System untuk mengontrol aktuator secara otomatis. Sistem real-time monitoring dapat diakses melalui Thingspeak dan juga MQTT. Dari hasil penelitian didapatkan nilai akurasi dari Real-time monitoring sebesar 97,93%. Untuk prosentase keberhasilan dari sistem berdasarkan tingkat hidup adalah 90% Sedangkan pertumbuhan Crystal Red Shrimp selama 3 minggu dengan nilai rata-rata 2,191 atau 0,191cm lebih optimal daripada yang melalui proses budidaya konvensional

MONITOREO DEL ÍNDICE DE CALIDAD DEL AGUA PARA CAMARONICULTURA POR MEDIO DE UN HARDWARE DE ACCESO ABIERTO Y UN SISTEMA DE INFERENCIA DIFUSA

La acuacultura de precisión es una nueva herramienta desarrollada en el campo de la tecnología de la información (TI) que permite al acuicultor tener un mejor control sobre los procesos de la granja, facilitar la toma de decisiones y mejorarla eficiencia de la actividad. El desarrollo de sistemas de monitoreo continuo son importantes para los cultivos acuícolas ya que estos pueden detectar condiciones no deseadas que puedan perjudicar los organismos. En este estudio, se valoran las plataformas de hardware abierto e inteligencia artificial como alternativa para desarrollar nuevos sistemas de monitoreo. El sistema que se propone registra de manera automática las variables fisicoquímicas del agua (oxígeno disuelto, temperatura y pH) y las procesa mediante lógica difusa (inteligencia artificial) para la determinación del índice de calidad de agua. El sistema fue probado mediante un cultivo de camarón (Litopenaeus vannamei) con una talla de 1.67±0.23g en un periodo de 84 días. Los resultados demuestran que el sistema analiza las variables fisicoquímicos más importantes de un cultivo de camarón y fue capaz de calificar el índice de calidad de agua como: pobre, regular, buena y excelente en función de los umbrales óptimos requeridos por el cultivo. Esto indica que es posible el uso del sistema de hardware abierto y lógica difusa para el monitoreo del índice de calidad de agua y su aplicación en la acuacultura

ASSESSING WATER QUALITY INDEX IN RIVER BASIN : FUZZY INFERENCE SYSTEM APPROACH

Water Quality Index is an important water assessment that sustain and conserve the aquatic ecosystem. In Malaysia, the current classi ication practice on Department of Environmental Water Quality Index (DOE WQI) shows rigid value in term of assessing the input of parameters that close to a class boundary. Hence, this study proposed a technique to assess the parameters in a holistic manner by using the Fuzzy Inference System (FIS). The approach as an assessment tool represents the classes of various ranges and aggregating the parameters using membership function and Centroid Function respectively. A numerical example based on actual data from one of the sampling station from Inanam Likas River Basin was adapted in this study. It was adapted to demonstrate the proposed approach. Findings shown using the proposed methods indicate that the river has Poor water status. Overall, FIS is able to assess the parameters and execute into a single index that represent the condition from poor to excellent scales of the water qualit

Monitoring of water quality in a shrimp farm using a FANET

This paper develops an architecture for flying ad-hoc networks (FANETs) to enable monitoring of water quality in a shrimp farm. Firstly, the key monitoring parameters for the characterization of water quality are highlighted and their desired operational ranges are summarized. These parameters directly influence shrimp survival and healthy growth. Based on the considered sensing modality, a reference architecture for implementing a cost-effective FANET based mobile sensing platform is developed. The controlled mobility of the platform is harnessed to increase the spatial monitoring resolution without the need for extensive infrastructure deployment. The proposed solution will be offered to shrimp farmers in the Mexican state of Colima once the laboratory trials are concluded

Modelo basado en redes neuronales artificiales para la evaluación de la calidad del agua en sistemas de cultivo extensivo de camarón

El cultivo de especies acuícolas es una actividad comúnmente practicada alrededor del mundo. En México, el cultivo de camarón es una de las principales fuentes de ingresos en el área de la acuicultura. La calidad del agua es un factor relevante en el éxito del cultivo en granjas camaronícolas, por lo que su monitoreo resulta ser de vital importancia. Este estudio presenta un nuevo modelo computacional para la evaluación de la calidad del agua en granjas de cultivo extensivo para camarón Litopenaeus vannamei. Mediante el uso de las redes neuronales artificiales se creó un indicador de la calidad del agua, mismo que permite establecer una relación entre la dinámica de los parámetros del ecosistema y diferentes estados para el cultivo de la especie (excelente, bueno, regular y deficiente). Se seleccionaron cuatro parámetros medioambientales debido a su importancia en el hábitat: temperatura del agua, pH, oxígeno disuelto y salinidad. Los resultados obtenidos muestran un buen funcionamiento y eficiencia por parte del sistema propuesto, al compararlo con otros modelos de evaluación empleados para este fin. Las evaluaciones muestran a las RNA como una buena opción para la evaluación y detección de estados óptimos o no deseados para un buen manejo del agua en este tipo de cultivos

Diseño y construcción de un sistema de supervisión para la evaluación de la calidad del agua en sistemas de cultivo de camarón

Actualmente el cultivo de especies acuícolas para consumo humano es una práctica que se realiza en todo el mundo. La evaluación de la calidad del agua es un procedimiento indispensable, debido a que un adecuado control de la misma permite mayores tasas de crecimiento y producción. El presente trabajo propone el desarrollo de una tarjeta digital para el monitoreo y evaluación de la calidad del agua en estanques de cultivo de camarón de la especie “Litopenaeus Vanammei”. Mediante la supervisión de un conjunto base de parámetros físico-químicos, se establecen pesos que determinan aquellos con mayor importancia y por lo tanto con una mayor afectación en el ecosistema. Asimismo, se presenta un nuevo modelo computacional para la evaluación de la calidad del agua empleando el método de Procesos Analítico Jerárquicos. Resultados experimentales muestran un buen desempeño del dispositivo propuesto, empleando comparaciones contra los estándares más destacados en el campo de la acuacultura.Palabra(s) Clave(s): acuacultura, adquisición, camarón, evaluación, tarjeta

An intelligent classification system for land use and land cover mapping using spaceborne remote sensing and GIS

The objectives of this study were to experiment with and extend current methods of Synthetic Aperture Rader (SAR) image classification, and to design and implement a prototype intelligent remote sensing image processing and classification system for land use and land cover mapping in wet season conditions in Bangladesh, which incorporates SAR images and other geodata. To meet these objectives, the problem of classifying the spaceborne SAR images, and integrating Geographic Information System (GIS) data and ground truth data was studied first. In this phase of the study, an extension to traditional techniques was made by applying a Self-Organizing feature Map (SOM) to include GIS data with the remote sensing data during image segmentation. The experimental results were compared with those of traditional statistical classifiers, such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance classifiers. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification (with respect to the period of inundation by regular flooding) was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers. It also achieved higher accuracies for more classes in comparison to the other classifiers. However, it was also observed that different classifiers produced better accuracy for different classes. Therefore, the investigation was extended to consider Multiple Classifier Combination (MCC) techniques, which is a recently emerging research area in pattern recognition. The study has tested some of these techniques to improve the classification accuracy by harnessing the goodness of the constituent classifiers. A Rule-based Contention Resolution method of combination was developed, which exhibited an improvement in the overall accuracy of about 2% in comparison to its best constituent (SOM) classifier. The next phase of the study involved the design of an architecture for an intelligent image processing and classification system (named ISRIPaC) that could integrate the extended methodologies mentioned above. Finally, the architecture was implemented in a prototype and its viability was evaluated using a set of real data. The originality of the ISRIPaC architecture lies in the realisation of the concept of a complete system that can intelligently cover all the steps of image processing classification and utilise standardised metadata in addition to a knowledge base in determining the appropriate methods and course of action for the given task. The implemented prototype of the ISRIPaC architecture is a federated system that integrates the CLIPS expert system shell, the IDRISI Kilimanjaro image processing and GIS software, and the domain experts' knowledge via a control agent written in Visual C++. It starts with data assessment and pre-processing and ends up with image classification and accuracy assessment. The system is designed to run automatically, where the user merely provides the initial information regarding the intended task and the source of available data. The system itself acquires necessary information about the data from metadata files in order to make decisions and perform tasks. The test and evaluation of the prototype demonstrates the viability of the proposed architecture and the possibility of extending the system to perform other image processing tasks and to use different sources of data. The system design presented in this study thus suggests some directions for the development of the next generation of remote sensing image processing and classification systems

GIS-based modelling of agrochemical use, distribution and accumulation in the Lower Mekong Delta, Vietnam: A case study of the risk to aquaculture

In recent years, the Mekong delta has been strongly developed both for agriculture and aquaculture. However, there is scope for a negative impact of agriculture on aquaculture in term of production and quality of seafood products. Specifically, the large amount of pesticides imported and used in the Mekong delta not only help agriculture purposes but can also easily enter aquatic systems and affect aquaculture. Pesticides can be transported in the environment by chemo-dynamic procedures and hydrological processes. As a result, pesticides used in agriculture become dispersed and their residues in sediment, water and biota have been detected in the Mekong delta. This study investigated the overall pesticide process including pesticide use, modelling pesticide accumulation and evaluating the potential impact on aquaculture sites for some target aquatic species. The risk of pesticides use in the Mekong delta was addressed in three stages: (1) investigating current pesticide use status in the Mekong delta; (2) modelling pesticide loss and accumulation; (3) classifying pesticide risk areas for aquaculture of target cultured species. A survey of 334 farms covering a total area of ~20,000km2 in the Mekong delta took place between 2008 and 2009. Information on pesticide types and quantities was recorded using questionnaires, and it was found that 96 pesticides in 23 groups were popularly used for agricultural purposes. Dicarboximide, Carbamate and Conazole had the highest use at ~3000, ~2000 and ~2000 g/ha/year respectively. The survey revealed an increase in pesticide use per hectare since previous surveys in the Mekong delta in 1994, 2000, and 2004. However, the highly persistent compounds (WHO classification classes II, III and IV) appeared to have reduced in use. Insecticides previously represented >50% of the total pesticides used, however, the resent survey has shown their use has decreased to ~38%.There was a parallel increase in use of fungicides from previous levels of <30% of total pesticides to more recently ~41%. The combination of pesticide information and geo-location data enabled display and analysis of this data spatially using a Geographic Information System (GIS). A pesticide loss and accumulation model was established through combination of several sub-models including sediment loss and accumulation, direct loss, and water runoff, all of which were implemented and integrated within the GIS environment. MUSLE (Modified Universal Soil Loss Equation) was used to estimate sediment loss and accumulation in the Mekong delta and the Curve Number method (CN Method) was applied to predict water runoff and discharges and flow accumulation. Modelling commenced from the first pesticide application in April, based on 4 day time-steps. All mathematical calculations run within each time step automatically reiterated in the following time step with the new input datasets. The results from fuzzy classification of the pesticide model outcomes were considered in terms of the 96hr lethal concentration (LC50) in order to classify the risk and non-risk areas for catfish and tiger shrimp culture. The sediment loss and accumulation model shows that the highest loss of sediment was in the rainy season, especially in May to October. Vegetables and short term crop areas were found be most strongly eroded. The MUSLE model showed that the highest sediment accumulation was in the hilly areas (~1066.42 tonne/ha/year); lower in riverside areas (~230.39 tonne/ha/year) and lowest in flooded paddy areas (~150.15tonne/ha/year). Abamectin was used as an example throughout this study to estimate pesticide loss and its effects on aquaculture. The results showed that pesticide loss by runoff and sediment loss is less than the loss by half-life degradation (for Abamectin specifically). Accumulation of Abamectin occurred at highest rate in May and October and decreased with time. The spatial models showed that pesticide residues concentrated in the river and riverside areas. In order to evaluate the acute toxicity impacts, three levels of water depth in ponds were modelled as culture depths for catfish and tiger shrimp. The results show that the highest risk areas for catfish occurred in May and October with ~333,000 and ~420,000 ha at a pond depth of 0.5 m; ~136,000 and ~183,000 ha at a pond depth of 1.0 m; and ~10,840 and ~19,000 ha at a pond depth of 1.5 m. Risk areas for catfish mainly concentrated at the riverside and in part of the coastal areas. For tiger shrimp, the risk periods during the year were similar to those found for catfish. The highest risk areas for shrimp were ~648,000 and ~771,000 ha at 0.5 m pond depth; ~346,000 and ~446,700 ha at 1.0 m pond depth; and ~185,000 and ~250,000 ha at 1.5 m pond depth. Overall, deeper ponds reduced the risk. This study has developed a method to evaluate the negative impact of input pesticides to the environment from agricultural use related to fluctuation of aquaculture risk areas. The research indicates the potential relationship between pesticide input and the risk areas for aquaculture. The model has several significant uses: 1) it can provide information to policy makers for a more harmonized development of both aquaculture and agriculture in the Mekong delta in the future, 2) it provides data for aquaculture investment analysis to decrease the hazards caused by pesticide impacts, and 3) it provides a model capable of application to wide field scenarios and suitable for any pesticide type