Non-destructive sensing for determining Sunagoke moss water content -bio-inspired approaches-

One of the primary determinants of Sunagoke moss Rachomitrium japonicum growth is water availability. There is need to develop non-destructive sensing of Sunagoke moss water content to realize automation and precision irrigation in a close bio-production system. Machine vision can be utilized as non-destructive sensing to recognize changes in some kind of features that describe the water conditions from the appearance of wilting Sunagoke moss. The goal of this study is to propose and investigate bio-inspired algorithms i.e. neural-genetic algorithms (neural-GAs) and neural-ant colony optimization (neural-ACO) to find the most significant set of image features suitable for predicting cultured Sunagoke moss water content in a close bio-production system. Features extracted consisted of 13 colour features, 90 textural features (grey level co-occurrence matrix, RGB, HSV and HSL colour co-occurrence matrix textural features) and three morphological features. Each colour space consisted of ten textural features algorithms: entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment and cluster tendency. The specificity of this problem was that we were not looking for single image feature but several associations of image features that may be involved in determining water content of Sunagoke moss. Neural-ACO had better prediction performance with lower number of features than neural-GAs. The minimum validation prediction mean square error (MSE) achieved was 2.02x10-3 when using 10 relevant features

Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behaviour of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for solving discrete optimization problem. We test the algorithm to solve bio-computation problem using single and multi-objectives optimization. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discrete-particle swarm optimization (discrete-PSO)

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442

A fuzzy micro-climate controller for small indoor aeroponics systems

The Indonesian agricultural sector faces challenges producing affordably priced food using sustainable practices. A soilless cultural practice, such as indoor aeroponics, is a compelling alternative to conventional agriculture. The objective of the present study was to develop a system for micro-climate management in a pilot-scale indoor aeroponics system. For this purpose, three fuzzy logic controllers were developed and evaluated to maintain plant chamber parameters (temperature, relative humidity, and light intensity) at desired set points controlled by embedded system controls designed using BASCOM-AVR software. The results showed that the fuzzy controllers provided excellent responses and experienced relatively low errors in all controlled parameters. All parameters changes followed the set point very smoothly and responded accordingly.The averaged percent of working times in which temperature, relative humidity, and light intensity were maintained within less than ±1°C, ±5%, and ±30 lux from the set points were found to be 88.43%, 95.91%, and 85.51%, respectively

A rapid classification of wheat flour protein content using artificial neural network model based on bioelectrical properties

A conventional technique of protein analysis is laborious and costly. One rapid method used to estimate protein content is near infrared spectroscopy (NIRS), but the cost is relatively expensive. Therefore, it is necessary to find a cheaper alternative measurement such as measuring the bioelectrical properties. This preliminary study is a new rapid method for classified modeling of wheat flour protein content based on the bioelectrical properties. A backpropagation artificial neural network (ANN) was developed to classify the protein content of wheat flour. ANN input were bioelectrical properties, namely capacitance, and resistance and output was a type of the flour, namely hard, medium and soft flour. The result showed that the ANN model could classify the various type of flour. The best ANN model produces a mean square error (MSE) and regression correlation (R) of 0.0399 and 0.9774 respectively. This ANN model could classify the protein content of wheat flour based on the bioelectrical properties and have the potential to be used as a basic instrument to estimate the protein content

Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach

This study aimed to determine the nitrogen content of spinach leaves by using computer imaging technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural features constructed from RGB and grey colors. From the 40 textural features, the best features-subset was selected by using features selection method. Features selection method can increase the accuracy of image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of ANN. The computer vision method using ANN model which has been developed can be used as non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate application of their nitrogen fertilization strategies using low cost computer imaging technology

Rancang Bangun Fermentor Yogurt dengan Sistem Kontrol Logika Fuzzy Menggunakan Mikrokontroler ATMega32

Yogurt is milk fermented product that becomes popular recently. In yogurt processing, fermenter is the main device. Lactobacillus sp. and Streptococcus sp. are two probiotic bacteria species that are common to be used in yogurt fermentation process. Both bacteria grow well in a specific range of temperature between 40-45 C, so temperature control in fermenter operational becomes one of the important things to ensure speed and quality of fermentation process. Fermentation process is a process with high degree of uncertainty and categorized as non-linear time invariant system. Thus, classical control system method is difficult to be implemented. To overcome this issue, intelligent control system can be implemented to yogurt's fermenter temperature control. One of intelligent control system method that can be implemented is fuzzy logic-based control system. In this study, fuzzy control system has been designed and implemented for fermenter temperature control. Control system algorithm is integrated in ATMega16 (for On-Off logic control) and ATMega32 (for Fuzzy Logic control) microcontrollers. Experimental results of fermenter control system shows that temperature profile of fermenter with fuzzy logic control system is more stable by settling time around an hour and 15 minutes and error average of -0.36 oC. Fermentation process for 16 hours with fuzzy logic controller produce yogurt with pH value of 3.66, total number of Lactobacillus sp. is 4.85 x 10 cfu/mL and Streptococcus sp. is 1.34 x 106 cfu/mL

ANALISA TARIKAN PERGERAKAN LALU LINTAS SEBELUM DAN SESUDAH PEMBANGUNAN UNDERPASS SIMPANG PATAL PALEMBANG

ABSTRAKProyek pembangunan Underpass adalah suatu alternatif yang dapat dilakukan untuk mengatasi kepadatan kendaraan pada suatu titik akibat adanya pertemuan empat arah ruas jalan yang berbeda. Oleh karena itu salah satu solusinya yaitu dengan membangun underpass sebagai upaya yang bisa mengurangi kepadatan kendaraan yang mengakibatkan kemacetan lalu lintas. Berkaitan dengan kemacetan lalu lintas pemerintah harus peka dalam menindaklanjuti masalah kemacetan lalu lintas, diantaranya dengan cara menghitung jumlah lalu lintas harian rata-rata (LHR) yang melintasi kawasan pembangunan Underpass. Sehingga bisa digunakan untuk melakukan penambahan volume jalan akan menampung kendaraan yang melintasi kawasan tersebut. Jadi dalam penelitian ini penulis akan menganalisa dan menjelaskan bagaimana dampak proyek pembangunan Underpass terhadap kemacetan lalu lintas. Hasil penelitian ini akan didukung dengan data hasil perbandingan jumlah volume kendaraan sebelum dan sesudah dibangun Underpass. Kata Kunci : Kapasitas Jalan, Volume Kendaraan dan Tingkat Pelayanan Jala

DETEKSI PEMALSUAN KOPI LUWAK MENGGUNAKAN SIFAT BIOLISTRIK DAN JARINGAN SARAF TIRUAN

Metode konvensional deteksi pemalsuan kopi luwak menggunakan analisis kimia bersifat destruktif, mahal, membutuhkan preparasi sampel dan waktu lama. Perancangan alat sederhana, cepat, akurat, dan non destruktif berdasarkan sifat biolistrik berpeluang untuk mendeteksi pemalsuan kopi luwak. Penelitian ini bertujuan mendapat topologi Jaringan Saraf Tiruan (JST) terbaik untuk mendeteksi pemalsuan kopi luwak menggunakan input sifat biolistrik berdasarkan total fenol, pH, dan persentase pemalsuan kopi luwak. Hasil penelitian menunjukkan bahwa impedansi, resistansi seri, resistansi paralel berbanding terbalik dengan frekuensi, induktansi seri dan induktansi paralel berbanding lurus dengan frekuensi. Topologi JST terpilih yaitu (5-40-40-3) memiliki MSE pelatihan 0.0099 dan MSE validasi 0,0479. Hasil penelitian menunjukkan sifat biolistrik dan JST berpotensi sebagai sensor mendeteksi pemalsuan kopi luwak