Judul: The Development of Soybean Grading Algorithm Using Image Processing and Artificial Neural Network
Abstrak: Grading method influences the homogeneity of each grade of such product and a good grading method provides producer and consumer with standard for the price of the product. The objective of this research was to develop a computer program of image processing and artificial neural network to identify the quality of fresh soybean into four classes namely SQ (standart quality), SG (second grade), TG (third grade), and RJ (reject) using image processing and artificial neural network. The total samples were 2500 fresh soybean produced by PT. Mitra Tani Dua Tujuh Jember. Soybean image was analyzed to get six quality parameters whose match with soybean quality criteria namely pod length, pod area, perimeter, defect area, index of red color, and index of green color. Those six quality parameters will be used as inputs of the artificial neural network (ANN). Six variations of ANN were developed for ANN training purposes (2000 data). The weights of the selected ANN architecture was used to identify the quality class of testing data (500 data), then integrated with image processing program so the program could identify soybean quality class automatically. The quality parameter used in this research has relevancy with soybean quality criteria. The selected architecture of the ANN was the one with 20 nodes hidden layer in which normalization input data representation with zero mean and standard deviation equals one. The accuracy of image processing program observed 81, 4 percent based on the 500 testing data.
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Judul: Development of grading system based on image processing and artificial neural network for green coffee sorting equipment with belt konveyor type
Abstrak: The objective of this research was to develop a grading system which consisted of a computer program of image processing and artificial neural network to identify quality of green coffee namely A, B, C, and RJ (reject). Two images of the green coffee from top and bottom captured by two cameras were analyzed to get six quality parameters which matched the green coffee quality criteria namely length, area, perimeter, defect area, index of red color, and index of green color. Those six quality parameters were used as training data inputs (75% - 768 kernel) of the developed artificial neural network (ANN). Eighteen variations of ANN were developed for ANN training purposes. The weight of the selected ANN architecture were used to identify the quality class of testing data (25%), and then integrated with image processing program so the program could identify green coffee quality class automatically. The total accuracy of ANN was 67% from top camera with A 59%, B 53%, C 70%, RJ 84%, and the total accuracy was 71% from bottom camera with A 75%, B 45%, C 73%, and RJ 92% based from 256 testing data. The total accuracy of ANN from combination of both cameras was 68%. New training was developed in order to increase prediction accuracy by modified ANN inputs. Prediction accuracy produced by the new ANN weight were A 78%, B 53%, C 70%, RJ 98%, and the total accuracy was 75%
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Judul: Prevalence of Bird Lice on Rock Pigeon (Columba livia) in 99Wonderland Park, Selangor, Malaysia
Abstrak: Rock Pigeon (Columba livia) have coexisted with humans for thousands of years and are used for various purposes such as food and in sports. Rock pigeons may have the potential to be infested with a wide range of ectoparasites, which can have a significant impact on their life cycle. This study aims to measure the prevalence of Columba livia with bird lice in 99Wonderland Park, Selangor, Malaysia in the year 2020 and to ascertain the impact of bird lice on the health of these infested birds. Data were collected from clinical records for the month of January to December in the year 2020 from 99Wonderland Park, Selangor, Malaysia. Lice were identified in 42 of the 80 Rock pigeons examined so, the overall prevalence of lice infestations was 52.5%. The prevalence of ectoparasites was found to be higher in young Rock pigeons (60.4%) as compared to adult Rock pigeons (37.4%). Columbicola columbae (wing lice) and Colpocephalum turbinatum (body lice) are two species of lice that have been detected. Body lice were more prevalent in young Columba livia birds at 47.6% whereas wing lice were more prevalent in adult Columba livia birds at 16.7%. Most of the lice were found in the thorax and abdomen area followed by the head and neck area., Merpati Batu (Columba livia) telah hidup berdampingan dengan manusia selama ribuan tahun dan digunakan untuk berbagai keperluan seperti makanan dan olahraga. Merpati batu dapat terinfestasi dengan berbagai ektoparasit, yang dapat berdampak signifikan pada siklus hidup mereka. Penelitian ini bertujuan untuk mengetahui prevalensi Columba livia dengan kutu burung di 99 Wonderland Park, Selangor, Malaysia pada tahun 2020 dan untuk memastikan dampaknya terhadap kesehatan burung yang terinfestasi. Data dikumpulkan dari catatan klinis dari bulan Januari hingga Desember tahun 2020 dari 99Wonderland Park, Selangor, Malaysia. Kutu diidentifikasi pada 42 dari 80 merpati batu yang diperiksa sehingga, prevalensi keseluruhan infestasi kutu adalah 52,5%. Prevalensi ektoparasit ditemukan lebih tinggi pada merpati batu muda (60,4%) dibandingkan dengan merpati batu dewasa (37,4%). Columbicola columbae (kutu sayap) dan Colpocephalum turbinatum (kutu badan) adalah dua spesies kutu yang telah ditemukan. Kutu badan lebih banyak ditemukan pada burung Columba livia muda sebesar 47,6% sedangkan kutu sayap lebih banyak ditemukan pada burung Columba livia dewasa sebesar 16,7%. Sebagian besar kutu ditemukan di daerah dada dan abdomen diikuti daerah kepala dan leher.
Keyword: Columba livia, prevalence, ectoparasite, wing lice, body lice, Columba livia, prevalensi, ektoparasit, kutu sayap, kutu badan