Judul: Estimation of infestation level of diamondback moth and cabbage cluster caterpillar based on images
Abstrak: Diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), and cabbage cluster caterpillar, Crocidolomia pavonana (F.) (Lepidoptera: Pyralidae), are the two most important insect pests of cabbage in Indonesia. Research was conducted with the objectives to assess level of infestsation of the pests based on digital images. Infestation levels were classified into five categories: healthy/negligible, low, moderate, severe and very severe. The data used in this research were images of cabbage that were healthy and attacked by Plutella xylostella and/or Crocidolomia pavonana. Images of cabbages with crop and without crop were 476 and 24 images respectively. The images were divided in two sets: training set and test set with percentage are 80% and 20% respectively. Ten-fold cross-validation was used to find the best model for classifier. Estimation of infestation level initially was made on individual plants. Cabbages with crop and without crop were classified with probabilistic neural network (PNN) based on four Haralick features, namely: contrast, correlation, dissimilarity, and homogeneity. Gray level co-occurrence matrix (GLCM) was used to extract texture features. The research showed that the average accuracy from PNN as a classifier for classifying cabbage with crop and without crop was 92.4%. Cabbage with crop was segmented, and after that the crop area was detected with randomized hough transform (RHT). The detected crop then were grouped into three categories: good, good enough and not good. Our research showed that estimation of crop area with RHT had accuracy of 89.13 %. Image enhancement was then applied to the crop area through shadow removal and smoothing. The level of damage was estimated based one proportion of holes on the crop area by applying erosion operations and thresholding with Otsu. Estimation of level of damage for cabbage with crop had accuracy of 76.1%. Estimation of level of damage for cabbage with crop and without crop had accuracy of 75%. Having obtained the level of damage for each plant, the final stage of this research was to assess level of caterpillar infestation in cabbage fields. Error arising from this estimation was 3.25%. Results of this research could be used by pest observers or extension agents in making pest management decisions.
Keyword: Crocidolomia pavonana, gray level co-occurrence matrix, Plutella xylostella, probabilistic neural network, randomized hough transform

Judul: Techniques of Texture Analysis for Identification of Crocidolomia pavonana, Spodoptera exigua, Spodoptera litura
Abstrak: Cabbage and green onion are two common vegetables cultivated by farmers in highland. Among major pests causing significant damage are Crocidolomia pavonana on cabbage and Spodoptera exigua on green onion. In addition, the generalist Spodoptera litura occasionally found attacking these vegetables. For unexperienced farmers as well as extension agents, the three caterpillars are not easily differentiated; although an accurate identification is required for a better control of the pests. Therefore, research was conducted with the objectives to develop identification system of the caterpillars based on computer vision technology. The three species of caterpillars have a unique texture and distinct feature from each other; and therefore, differentiating the species can be based on techniques of texture analysis. For this purpose, we used combination of several texture analysis: entropy feature, GLCM, Haralick features and Tamura feature. Each species consisted of 45 images, and consequently the total images used in this research were 135 images. Data work splitted using k-fold cross validation technique into training data (80%) and testing data (20%). Probabilistic Neural Network (PNN) was applied for classifying the three species of caterpillars. Research was conducted using GLCM with 1 pixel distance and 8 angles; Haralick features consisting of entropy, homogeneity, information of correlation 1, information of correlation 2; and Tamura feature consisting of coarseness; and entropy feature techniques. Our research revealed that Tamura features and information of correlation 1 were able to differentiate S. litura from other species. Entropy feature was able to differentiate C. pavonana with S. litura. Haralick entropy was able to differentiate C. pavonana with S. Exigua. Homogeneity was able to differentiate S. Exigua with S. litura whereas information of correlation 2 was not able to differentiate among the three species. However, combination of four techniques were able to improve the accuracy of caterpillar identification system. System performance obtained from the average accuracy of the entire fold reached 77.03%. The best accuracy (88.89%) was obtained when k = 5. Furthermore, we tested the system using classification model based on the fifth fold. Error occurred when C. pavonana incorrectly identified as S. exigua, and S. exigua incorrectly identified as S. litura. The error in identification were caused by inappropriate cropping of caterpillar images, the blur of image, and the caterpillar was not in advance stage. However, in general the pest identification system developed through this research can be used by farmers and extension agents who have difficulties in differentiating the three species of caterpillars.
Keyword: textures analysis, computer vision, pest identification

Judul: Formulasi Hamiltonian untuk Menggambarkan Gerak Gelombang Soliter Dimensi Tiga di Permukaan Laut
Abstrak: Gelombang permukaan adalah gelombang yang terjadi perbedaan rapat massa antara air dan udara. Salah satu gelombang permukaan yang ditinjau adalah gelombang soliter. Formulasi gelombang soliter dilakukan dengan menggunakan formulasi Hamiltonian.
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