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All Journal Jurnal Ilmu Pertanian Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
AMIN NUR
Balai Penelitian Tanaman Serealia, Jl. Dr. Ratulangi No. 274, Maros 90514
Agriculture, Biological Sciences & Forestry Computer Science & IT

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Evaluation of drought-tolerance in some tropical wheat genotypes (Triticum aestivum L.) at different osmotic-stress levels Nur, Amin; Kadir, Muhammad; Kaimuddin, Kaimuddin; Musa, Yunus; Badaruddin, Muh Farid
Jurnal Ilmu Pertanian Vol 5, No 2 (2020): August
Publisher : Faculty of Agriculture, Universitas Gadjah Mada jointly with PISPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4532.471 KB) | DOI: 10.22146/ipas.46435

Abstract

Abiotic factors, such as temperature and drought, were the main factors limiting the cultivation under the tropical condition. Two-stage experiments were conducted to examine the drought-tolerant potential of some wheat genotypes against the osmotic stress under the tropical condition at the Laboratory and Greenhouse of Hasanuddin University and Indonesian Cereal Research Institute. The experiments were arranged in a randomized block design with the split-plot pattern and respectively provided with four and three replications. The main plot was potential osmotic stress (0, -0. 33 , and -0.67 MPa) and the sub-plot was selected wheat genotypes (17 genotypes). The results indicated that based on the germination percentage, shoot/root ratio, proline content, stomatal behavior, and relative water content, the wheat lines of O/HP-78-A22-3-7, WBLL*2KURUKU, O/HP-6-A8-2-10, and O/HP-22-A27-1-10 were identified to have better drought-tolerance than the others genotypes based on the analysis of responses to parameters observed. The positively adaptive response of some tropical wheat genotypes to drought stress may be used as a potential donor for further development of drought-tolerant wheat varieties under the tropical climate in Indonesia. 
Corn Seeds Identification Based on Shape and Colour Features Yafie, Haddad Alwi; Rachmawati, Ema; Prakasa, Esa; Nur, Amin
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.10840

Abstract

Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.
Co-Authors Badaruddin, Muh Farid Esa Prakasa, Esa Kaimuddin Kaimuddin, Kaimuddin Muhammad Kadir Yafie, Haddad Alwi YUNUS MUSA