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For anyone who is interested in our graduate programs at the Department of Chemistry, Faculty of Science, Chulalongkorn University, you can now watch a recorded live “Active Recruitment 2022” program
- พบกับกิจกรรมของภาควิชาเคมีในงาน Sci Chula Open House 2023 Mar 29, 2023
- Meet… BSAC Alumni Warongvat Wanachaikiat BSAC#06 Dec 13, 2022
- Meet… BSAC Alumni Dr.Theeraya Krisdaphong BSAC#02 Dec 13, 2022
- Seminar & workshop under the theme “Chula Sustainnovation Series, Episode 1: Ocean Plastics” Nov 22, 2022
- Congratulations to new graduates (B.Sc., M.Sc., and Ph.D.) from our department! Nov 12, 2022
- Lecture on “How to handle and store chemicals in warehouse safely” Sep 25, 2022
- งานปฐมนิเทศนิสิตใหม่ปริญญาตรีชั้นปีที่ 1 Jul 7, 2022
- รับสมัครทุนพสวท.สำหรับนิสิตปริญญาบัณฑิตศึกษา Jul 6, 2021
- ปฐมนิเทศนิสิตใหม่ ระดับปริญญาตรี Jun 16, 2021
- ยินดีต้อนรับว่าที่นิสิตใหม่จากระบบ TCAS 64 May 27, 2021
- The admission for graduate study in green chemistry and sustainability for Round 2 of the first semester academic year 2023 is now open. Mar 19, 2023
- The admission for graduate study in chemistry for Round 2 of the first semester academic year 2023 is now open. Mar 19, 2023
- The admission for graduate study in chemistry for Round 1 of the first semester academic year 2023 is now open. Feb 1, 2023
- The admission for graduate study in green chemistry and sustainability for Round 1 of the first semester academic year 2023 is now open. Feb 1, 2023
- The ChemCU Graduate Symposium for Green Chemistry & Sustainability graduate students Dec 20, 2022
Weedy rice is one of the most problematic weeds in rice-growing regions, particularly in Southeast Asia. Unlike other types of weeds, it is extremely difficult to distinguish weedy rice from cultivated rice directly from paddy seeds as they exhibit common morphological features. As such weed management can be a difficult, time-consuming, and inaccurate process that is often carried out manually. This study offers a novel classification approach based on an artificial neural network (ANN), utilizing self-organizing maps (SOMs), to directly discriminate weedy rice using the near-infrared (NIR) hyperspectral imaging (HSI) technique. Classification accuracies of 98% (Weedy vs PL2) were obtained with balanced sensitivity and specificity. The classification was assessed from the whole sample image, which was completely independent of the selected region of interest. This is the first instance where SOMs have been utilized to appraise seed quality by means of authentic HSI images.
