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All Journal International Journal on Food, Agriculture and Natural Resources (IJFANRES) International Applied Science
Chatchawan Chaichana
Chiang Mai University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Medicine & Pharmacology

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Effects of Various Hydroponic Systems in Increasing Caisim (Brassica Chinensis L.) Productivity Under LED Grow Light Braja Manggala; Malinny Debra; Chatchawan Chaichana; Wahyu Nukholis Hadi Syahputra; Musthofa Lutfi
International Journal on Food, Agriculture and Natural Resources Vol 4, No 2 (2023): IJ-FANRes
Publisher : Food, Agriculture and Natural Resources - NETWORKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.
Identification of Long Bean Seed Varieties Using Digital Image Processing Coupled With Neural Network Analysis Wahyu Nurkholis Hadi Syahputra; Dandi Citra Nugraha; Abdul Jalil; Chatchawan Chaichana
International Applied Science Vol. 1 No. 2 (2022)
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/ias.v1i2.164

Abstract

Identification of long bean seed varieties can be used to save plant variety and intellectual property rights. Using digital image processing combined with artificial neural networks (ANN) has a possibility to recognize the seed morphology. The purpose of this research is to identify the image variables that can be used to identify long bean seed varieties so that the best algorithm of artificial neural networks can be arranged and the level of accuracy in expecting the long bean varieties. The samples used in this study were long bean seeds of parade tavi, kanton tavi, branjangan, and petiwi varieties. For each variety, 400 samples were taken for training data and 200 samples for testing data, so the total sample was 2400 long bean seeds. The research stages include image acquisition, image retrieval, image variable estimation, image processing program development, data analysis, ANN training, long bean variety identification program preparation, and program validation. The results showed that ANN with 10 hidden layers is the best model to develop a long bean seed identification. The identification program of long bean seed varieties resulting from the integration of image processing with artificial neural networks has an accuracy of 99.75%.
Co-Authors Abdul Jalil Braja Manggala Dandi Citra Nugraha Malinny Debra Musthofa Lutfi Wahyu Nukholis Hadi Syahputra Wahyu Nurkholis Hadi Syahputra