Yusuf Hendrawan

Neural-Genetic Algorithm as Feature Selection Technique for Determining Sunagoke Moss Water Content

Abstract This study investigated the use of machine vision for monitoring water content in Sunagoke moss. The main goal is to predict water content by utilizing machine vision as non-destructive sensing and Neural-Genetic Algorithm as feature selection techniques. Features extracted consisted of 13 colour features, 90 textural features and three morphological features. The specificities of this study was that we were not looking for single feature but several associations of features that may be involved in determining water content of Sunagoke moss. The genetic algorithms successfully managed to select relevant features and the artificial neural network was able to predict water content according to the selected features. We propose neural network based precision irrigation system utilizing this technique for Sunagoke moss production.

Intelligent Irrigation Control Using Color, Morphological and Textural Features in Sunagoke Moss

A non-invasive sensing technique for monitoring Sunagoke moss water conditions was proposed. This paper describes the design and development of precision irrigation control method by incorporating color, morphology and RGB color co-occurrence matrix (CCM) textural features. The objective of this study was to develop a model of artificial neural network and made comparison analysis of the color, morphology and textural features to determine appropriate combination of pictorial features to accurately predict water content. Optimum condition of Sunagoke moss based on photosynthesis rate, color features, morphological features and textural features can be achieved between 2 gg-1– 2.5 gg-1 water content. Neural network model performance was tested successfully to describe the relationship between water content and image features (color, morphology and textural features). This system is helpful to explore the new way of water spraying in moss plant factories based on computer vision. It proposes the water irrigation technology of the plant factory to realize the automation and precision farming. Precision water and nutrition spraying system based on computer vision is very important, not only for spraying the water and nutrition scientifically, but also for improving the efficiency of spraying and decreasing the non- or off-target of moss to prevent from over watering.

Determining an ANN pre-treatment algorithm to predict water content of moss using RGB intensities

Abstract Sunagoke moss is one of the plant products that are cultivated in a plant factory. One of the primary determinants of moss growth is water availability. The present work attempts to apply precision irrigation system using machine vision in plant factories. The specific objective was to evaluate the ability of bio-inspired approaches as pre-treatment algorithm of Artificial Neural Network (ANN) for determining water content of moss. The results showed that ANN was capable for predicting water content of moss using RGB intensities, and then some bio-inspired approaches such as Honey Bees Mating Optimization (HBMO), Ant Colony Optimization (ACO), Genetic Algorithms (GAs), Simulated Annealing (SA) and Discrete Particle Swarm Optimization (DPSO) were capable of optimizing the feature selection process.

Water Irrigation Control for Sunagoke Moss Using Intelligent Image Analysis

Abstract A novel technique suitable for noninvasive measurements of moss water content is presented. In this paper, colour image sensing is applied for measuring moss water content. Sunagoke moss Rhacomitrium canescens has been utilized as an active greening material to mitigate the urban heat island effect. The goal of this paper is to develop an intelligent image analysis system for water irrigation optimal control in Sunagoke moss. The combination of RGB components (green:red ratio, blue index, blue value and green index) using statistical pattern recognition can estimate water content and define the distribution of water condition in every pixel of Sunagoke moss images. The combination of colour image sensing and Artificial Neural Network (ANN) successfully described the relationship between water content and colour features i.e. average green index, average blue index, blue mean value, browning area index, green canopy index and average green:red ratio. This system is helpful to explore a new way of water spraying in Sunagoke moss plant factories based on computer vision. We propose a water irrigation technology of plant factory to realize the automation and precision farming. Precision water spraying system based on computer vision is important, not only for spraying the water scientifically, but also for improving the efficiency of spraying and decreasing the non- or off-target spraying to prevent over watering.

Image Feature Selection in Machine Vision 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 method for sensing water content of cultured Sunagoke moss to realize automation and precision irrigation in a close bio-production systems. 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-Ant Colony Optimization, Neural-Simulated Annealing and Neural-Genetic Algorithms to find the most significant sets of image features suitable for predicting water content of cultured Sunagoke moss. Image features consist of 8 color features, three morphological features and 90 textural features (gray level co-occurrence matrix, RGB, HSV and HSL color co-occurrence matrix textural features). Textural features consist of energy, entropy, contrast, homogeneity, inverse difference moment, correlation, sum mean, variance, cluster tendency and maximum probability. The specificity of this problem is that we are not looking for single image feature but several associations of image features that may be involved in determining water content of Sunagoke moss. Neural-Ant Colony Optimization had the best performance as a feature selection technique. The minimum average testing prediction mean square error achieved was 1.75x10-3. There is significant improvement between method using feature selection and method without feature selection.

Development of Precision Irrigation System using Machine Vision in Plant Factory

In a plant factory, optimal control for obtaining higher yield, higher production efficiency, minimum waste, and better quality of plants is essential. Sunagoke moss is one of the plant products which are cultivated in plant factory. One of the primary determinants of moss growth is water availability. Hence, there is need to develop precision irrigation for moss production in plant factory. The present work attempted to develop machine vision-based micro-precision irrigation system to optimize water use in plant factory and maintain the water content of moss constantly in optimum growth condition. The specific objective of this study is to propose nature-inspired algorithms to find the most significant set of image features suitable for predicting water content of cultured Sunagoke moss. Feature Selection (FS) methods include Neural-Genetic Algorithms (N-GAs) and Neural-Discrete Particle Swarm Optimization (N-DPSO), Neural-Honey Bee Mating Optimization (N-HBMO) and Neural-Fish Swarm Intelligent (N-FSI). Image features consist of color features and textural features with the total of 212 features extracted from grey, RGB, HSV, HSL, L*a*b*, XYZ, LCH and Luv color spaces. Back-Propagation Neural Network (BPNN) model performance was tested successfully to describe the relationship between water content of Sunagoke moss and image features. FS methods improve the prediction performance of BPNN.

Sunagoke Moss Water Content Sensing Using Machine Vision-Texture Analysis and Bio-inspired Algorithms-

Abstract One of the primary determinants of Sunagoke moss Rachomitrium japonicum growth is water availability. Too much water or too little water can cause water stress in plants. Non-destructive sensing (machine vision using texture analysis) was developed for sensing water content of Sunagoke moss to realize automation and precision irrigation to stabilize the water content at optimum condition. The goal of this study is to propose and investigate bio-inspired algorithms i.e. Neural-Genetic Algorithms (N-GAs) and Neural-Ant Colony optimization (N-ACO) to find the most significant set of textural image features suitable for predicting cultured Sunagoke moss water content in a close bio-production system. Textural features consisted of 90 textural features included grey level co-occurrence matrix, RGB, HSV and HSL colour co-occurrence matrix textural features. Nonlinear relationships between textural features and water content were identified by Back-Propagation Neural Network (BPNN). The lowest average prediction Mean Square Error (MSE) based on average testing-set data was 4.79×10- 3 when using HSL co-occurrence matrix textural features as the input of BPNN. Based on testing-set data, N-ACO had better performance for predicting Sunagoke moss water content than N-GAs with the average testing-set MSE of 1.43×10 −3 .

Incorporating Farmer Behavior in Farm Mechanization Development – A Fuzzy AHP Approach

Farmer behavior is rarely considered in farm mechanization plan development in developing countries. Inadequate recognition concerning farmer behavior is influenced by the difficulties in assessing the farmer’s perception and calculating their perceived important criteria for selecting an appropriate farm machine. Consequently, governmental agencies have difficulties determining an appropriate farm machine that farmers can use optimally.