ORCID Profile
0000-0002-9392-7088
Current Organisations
Chiang Mai University
,
Masaryk University, Faculty of Social Studies
,
University of Melbourne
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Publisher: Elsevier BV
Date: 03-2011
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 03-2023
Publisher: International Journal on Food, Agriculture, and Natural Resources (IJFANRES), Jember University
Date: 25-06-2023
DOI: 10.46676/IJ-FANRES.V4I2.143
Abstract: Hydroponics has been proven to increase crop production, particularly for leafy vegetable families, significantly. In addition, the hydroponic system can assist farmers in managing water and nutrition as a result, this method is appropriate for sustainability as a real action to prevent further environmental damage caused by agricultural production. Several hydroponics systems have been invented however, to get high plant yields, a selection of the system must be done by looking at the characteristics of the cultivated plants. Furthermore, artificial environmental conditions, such as light, temperature, and humidity, must be adjusted to accommodate the plant's requirements in a closed hydroponic system. In this study, three hydroponics systems (i.e., wick technique, Nutrient Film Technique (NFT), and Deep Flow Technique (DFT)) were compared for morphology features, including the number of leaves, leaf width, plant height, wet root weight, and fresh weight. Caisim (Brassica chinensis L.) was grown on a single shelf this design was intended to maximize land utilization in a closed area. Caisim's growing condition was under blue-red LED light for 35 days with a 16-hour illumination time at a distance of 15 and 20 cm. At harvest time, Caisim morphology utilizing the NFT approach produced a more significant (P 0.05) result than the wick and DFT methods. Furthermore, on fresh weight, the LED at 15 cm outperformed the wick, DFT, and NFT at 20 cm by 20%, 47%, and 33%, respectively. According to the findings, the NFT approach combined with a 15 cm spacing distance or a light intensity of 250 PPFD was better and significantly impacted Caisim's shape.
Publisher: Routledge
Date: 16-10-2009
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 09-2002
Publisher: Elsevier BV
Date: 09-2003
Publisher: MDPI AG
Date: 10-07-2022
DOI: 10.3390/S22145161
Abstract: Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia's extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia's weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 11-2022
Publisher: Springer Science and Business Media LLC
Date: 23-11-2018
Publisher: Informa UK Limited
Date: 04-2010
Publisher: Elsevier BV
Date: 04-2020
DOI: 10.1016/J.SCITOTENV.2020.136577
Abstract: Microalgal biomass is often used as a raw material in methane production. Some microalgae possess a complex cell-wall structure which has a low degradability of microorganisms in anaerobic digestion. However, some microalgae produce glycolate, which is excreted outside the cell and can be used to produce methane under anaerobic condition. This research aims to investigate microalgal cultivation using wastewater to reduce nutrients and efficiently create glycolate. Two strains of microalgae (Acutodesmus sp. AARL G023, Chlorella sp. AARL G049) and two microalgal consortia were cultivated at dilutions of 0.5-fold (W50), 0.75-fold (W75) and undiluted wastewater (W100). The results showed that the microalgal consortium with undiluted wastewater (WCW100) consisted of Leptolyngbya sp. (30.4%), Chlorella sp. (16.1%) and Chlamydomonas sp. (52.2%), revealed the highest biomass productivity at 64.38 ± 14.54 mg·L
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 12-2001
Publisher: Informa UK Limited
Date: 03-2013
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 11-2022
No related grants have been discovered for Chatchawan Chaichana.