Sunagoke Moss Research Articles

Classification of water stress in cultured Sunagoke moss using deep learning

Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.

Sunagoke Moss (Racomitrium japonicum) Used for Greening Roofs Is Severely Damaged by Sclerotium delphinii and Protected by a Putative Bacillus amyloliquefaciens Isolate.

Mosses are ecologically important plants also used for greening, gardening, and decorative purposes. Knowledge of the microbial flora associated with mosses is expected to be important for control and preservation of global and local environments. However, the moss-associated microbial flora is often poorly known. Moss-associated fungi and bacteria may promote plant growth and pest control, but they may be alternative hosts for pathogens of vascular plants. In this study, the fungus Sclerotinia delphinii was identified for the first time as a pathogen that causes severe damage to Sunagoke moss (Racomitrium japonicum). This moss is used for greening roofs and walls of buildings in urban environments owing to its notable tolerance of environmental stresses. Inoculation with the S. delphinii strain SR1 of the mono- and dicotyledonous seed plants Hordeum vulgare, Brassica rapa var. pekinensis, Lactuca sativa, and Spinacia oleracea, in addition to the liverwort Marchantia polymorpha and the moss Physcomitrella patens, showed that the fungus has a wide host range. Colonization with SR1 progressed more rapidly in non-vascular than in vascular plant species. Studies with P. patens under controlled conditions showed that SR1 secreted a fluid during colonization. Treatment with the secretion induced production of reactive oxygen species in the moss. Endogenous peroxidase partially inhibited SR1 colonization of P. patens. A bacterial isolate, most likely Bacillus amyloliquefaciens, that coexists with R. japonicum was antagonistic to SR1 growth. Taken together, the present results suggest that fungal colonization of mosses may be prevented by a peroxidase secreted by the moss and an antagonistic bacterium coexisting in the moss habitat. The findings suggest that there is potential to apply biological control measures for protection of mosses against fungal pathogens.

Evaluation of indoor Thermal Environment on Sunagoke Moss Green Roof by Numerical Analysis

Evaluation of indoor Thermal Environment on Sunagoke Moss Green Roof by Numerical Analysis

Effects of convection heat transfer on Sunagoke moss green roof: A laboratory study

The application of green roof has been encouraged to mitigate urban heat island occurrence and to provide ecosystem services in urban areas since a few decades ago. Because very few literature on the usage of moss in green roof implementation is available, this paper introduces the thermal performance of Sunagoke (Racomitrium Canescens) moss green roof in addressing urban heat island effect. In addition, the insights of evaporation cooling on Sunagoke moss are also discussed. The experiments were performed inside the Artificial Climate Chamber with the ambient temperature and humidity set to 30°C and 70% RH, respectively, to simulate a typical summer environment in Japan. The effects of convection heat transfer by means of wind velocity and irradiance were tested on a dry Sunagoke model house, a moist Sunagoke model house, and a control model house. By altering the wind velocity from 0 to 3m/s, the convection heat was found to dominate the entire heat transfer in dry roof surfaces that lack evaporation (dry Sunagoke and control model house). In contrast, for the whole heat transfer process, the latent heat of moist Sunagoke green roof governed and diverted 70% by no-wind convection and 91% by wind-induced convection. The latent and convection heat of moist Sunagoke green roof were clarified to have inverse correlation to each other, and the diversion rose upon reaching wind-induced convection state. In terms of normalised temperature differences, the influences of the roof and atmosphere were balanced on dry Sunagoke moss in wind-induced convection, as the temperature ratios were close to 1. However, on moist Sunagoke moss, the evaporation consumed most of the heat, hence, the temperature ratio was lower than the other two model houses. The ratio further diminished near 0 in wind-induced convection. Nevertheless, the effects of wind velocity above 2m/s were clarified identical, since no significant changes were found in the convection heat transfer coefficient, surface temperature, conduction heat flux, and interior temperature afterwards.

Machine Vision Optimization using Nature-Inspired Algorithms to Model Sunagoke Moss Water Status

Machine vision has been widely implemented to monitor water status of plants. The performance of machine vision affects the prediction process of plant water status. Therefore optimization is needed to improve the performance of machine vision. The objective of this study is to optimize the performance of machine vision to model Sunagoke moss water status. Back Propagation Neural Network was used to model the relationship of image features and Sunagoke moss water status. Multi Objective Optimization (MOO) was used to select 212 image features to get maximum prediction accuracy and minimum number of features subset. Nine nature-inspired algorithms for optimization i.e . Genetic Algorithms (GAs), Discrete Particle Swarm Optimization (DPSO), Honey Bees Mating Optimization (HBMO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Intelligent Water Drops (IWD), Discrete Firefly Algorithm (DFA), Discrete Hungry Roach Infestation Optimization (DHRIO), and Fish Swarm Intelligent (FSI) were compared. The result shows generally that the prediction model using feature selection techniques achieved significant prediction accuracy, and the number of feature-subset, and was better than the model without feature selection to predict Sunagoke moss water status.

Study of the Applicability of CFD Simulation for Analysis of the Transpiration Properties in Sunagoke Moss

Abstract Urban heat island (UHIP) is one of the environmental problems in the urban and is a feature commonly observed in many cities. The implementation of roof top greening is one of effective options to increase the amount of well-watered vegetation. Sunagoke moss have attracted attention as cover plants recently because they have desiccation-tolerance and are easy to manage. In this study, we obtained knowledge of transpiration properties in Sunagoke moss to optimal control the irrigation system of moss roof.

Fungi infecting cultivated moss can also cause diseases in crop plants

AbstractBryophytes (mosses) are non‐vascular plants inhabited by a large number of fungal species, but whether mosses can act as reservoirs of fungal pathogens of crop plants has gained little attention. A few moss species including the Sunagoke moss (Racomitrium japonicum; family Grimmiaceae) are found to have modern economical applications in uses such as greening of urban environments. In a previous study, we identified fungi causing symptoms of varying severity in the commercially grown Sunagoke moss. The aim of this study was to test whether the same fungal isolates are pathogenic to vascular plants. An isolate of Fusarium avenaceum lethal to the Sunagoke moss caused root and crown rot in barley (Hordeum vulgare) and reduced germination of tomato (Solanum lycopersicum) and carrot (Daucus carota) grown in the infested soil. An isolate of Cladosporium oxysporum causing mild symptoms in moss reduced growth and caused reddening and premature death of carrot seedlings. On the other hand, isolates of Alternaria alternata and Fusarium oxysporum lethal to the Sunagoke moss caused no detectable symptoms in any tested vascular plant, suggesting specialisation of these isolates to moss. Chloroplast repositioning was observed in the neighbouring cells towards the initially infected cell following infection with F. avenaceum and A. alternata in Physcomitrella patens (family Funariaceae), a model moss used to study microscopic symptoms. Infection of P. patens with a non‐virulent Apiospora montagnei isolate induced formation of papillae in the moss cells, indicating activation of host defence as described in vascular plants. Results suggest that mosses and vascular plants may be linked by a common microbial interface constituted by pathogenic fungi. The findings have epidemiological implications that have gained little previous attention.

Neural-Intelligent Water Drops algorithm to select relevant textural features for developing precision irrigation system using machine vision

Sunagoke moss Rachomitrium japonicum is a good potential for greening material. One of the primary determinants of Sunagoke moss growth is water availability. Too much or too little water can cause water stress in plants. Water stress in plants can be detected by imaging. This study is part of on-going research aimed at developing machine vision-based precision irrigation system in a closed bio-production system for cultured Sunagoke moss. The objective of this study is to propose nature-inspired feature selection techniques to find the most significant set of Textural Features (TFs) suitable for predicting water content of cultured Sunagoke moss. The proposed Feature Selection (FS) methods include Neural-Intelligent Water Drops (N-IWD), Neural-Simulated Annealing (N-SA), Neural-Genetic Algorithms (N-GAs) and Neural-Discrete Particle Swarm Optimization (N-DPSO). TFs consist of 120 features extracted from grey, RGB, HSV, HSL and L ∗a ∗b ∗ colour spaces using ten Haralick’s textural equations. Back-Propagation Neural Network (BPNN) model performance was tested successfully to describe the relationship between water content of Sunagoke moss and TFs. Red Colour Co-occurrence Matrix (CCM) TFs, L ∗ CCM TFs, grey CCM TFs, value (HSV) CCM TFs, green CCM TFs and lightness (HSL) CCM TFs are recommended as individual feature-subset to be used for predicting water content of Sunagoke moss using Artificial Neural Networks. However, FS methods improve the prediction performance. The results show a significant difference between model using FS and models using individual feature-subsets or without FS. Comparative analysis shows the superiority of Neural-Intelligent Water Drops (N-IWD) compared to the other FS methods, since it achieve better prediction performance. The best N-IWD’s fitness function converged with the lowest validation-set Root Mean Square Error (RMSE) of 1.07 × 10 −2 when using 36 TFs.

Infection of the Sunagoke moss panels with fungal pathogens hampers sustainable greening in urban environments

Drought and heat tolerance of the Sunagoke moss ( Racomitrium japonicum) and the low thermal conductivity of the dry moss tissue offer novel greening and insulation possibilities of roofs and walls to mitigate the heat island phenomenon in urban environments. However, damage may appear in the moss panels under humid conditions in Japan. In this study we characterized fungi associated with the damaged areas of the Sunagoke moss panels. Fungi were identified by morphology and internal transcribed spacer (ITS) sequence analysis and tested for pathogenicity on R. japonicum (Grimmiaceae) and an unrelated moss species ( Physcomitrella patens; Funariaceae) under controlled conditions. Alternaria alternata, Fusarium avenaceum and Fusarium oxysporum caused severe necrosis and death, whereas Cladosporium oxysporum and Epicoccum nigrum caused milder discoloration or chlorosis in both moss species. The fungi pathogenic on moss were closely related to fungal pathogens described from cultivated vascular plants. Ammonium increased severity of fungal diseases in moss. This study demonstrated that fungi can cause economically significant diseases in cultivated moss and hamper commercial use of the moss panels unless appropriate control methods are developed. Use of a single moss clone to cover large surfaces and the air pollutants such as ammonium may increase the risk for fungal disease problems.

Bio-inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss

One of the primary determinants of Sunagoke moss Rachomitrium japonicum growth is water availability. There is need to develop a 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-Genetic Algorithms, Neural-Simulated Annealing and Neural-Discrete Particle Swarm Optimization to find the most significant sets of image features suitable for predicting water content of cultured Sunagoke moss. Image features consist of 8 colour features, three morphological features and 90 textural features (RGB, HSV, HSL colour co-occurrence matrix and gray level co-occurrence matrix textural features). Each colour space of 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 cultured Sunagoke moss. All feature selection models showed that prediction performance is getting better through all the iterations. It indicates that all models are effective. Neural-Ant Colony Optimization had the best performance as a feature selection technique. The minimum average prediction mean square error (MSE) achieved was 1.75 × 10 −3. There is significant improvement between method using feature selection and method without feature selection.

Development of Micro Precision Irrigation System in Plant Factory

AbstractComputer integrated systems and Artificial Intelligence (AI) have become an apparent need for control in a plant factory. Sunagoke moss Rachomitrium japonicum is one of the plant products which are cultivated in plant factory. One of the primary determinants of moss growth is water availability. The present work attempted to apply machine vision-based micro-precision irrigation system which is able to optimize water use in plant factory and maintain the water content of moss constantly in optimum growth condition. The 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. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. Feature Selection (FS) methods included Neural-Intelligent Water Drops (N-IWD), Neural-Simulated Annealing (N-SA), Neural-Genetic Algorithms (N-GAs), Neural-Ant Colony Optimization (N-ACO), Neural-Honey Bee Mating Optimization (N-HBMO), and Neural-Fish Swarm Intelligent (N-FSI). Image features consist of colour features and textural features with the total of 212 features extracted from grey, RGB, HSV, HSL, L*a*b* XYZ, LCH and Luv colour 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.

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

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.

Optimal Environmental Condition for Moss production in plant factory system

AbstractThe objective of this study is to propose a method for measuring the light reception of Sunagoke moss during the stirring process in the tank, and the optimal environmental condition for high speed production of moss in commercial plant factory system. The average amount of light reception of Sunagoke moss during cultivation in the tank was measured using integrated solarimeter film. The results shows large difference between the amounts of emitted light and received light. The various environmental composition found in this study can be used to optimize the environmental condition in commercial plant factory system.

Neural-Discrete Hungry Roach Infestation Optimization to Select Informative Textural Features for Determining Water Content of Cultured Sunagoke Moss

Neural-Discrete Hungry Roach Infestation Optimization to Select Informative Textural Features for Determining Water Content of Cultured Sunagoke Moss

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

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.

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

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.

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

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

Design of customisable greening material using swarm modelling

A greening material has different attributes for bio-physical, market and commercial functions. In designing a material, a plant factory has to select from a large set of initial design attributes. This paper presents swarm modelling (SM) to select the desired design attributes of customisable greening material. SM was developed by hybridising desirability model and particle swarm optimization (PSO). Design attributes were selected by predicting its consumer importance in a desirability model. Subsequently, PSO was used to optimise the model based on mentality constraints. SM was demonstrated on a case study of Sunagoke moss greening material ( Rhacomitrium japonicum ). The materials were classified into wet and semi-dry moss. The importance of a set of 24 attributes was predicted based on 15 mentality constraints. Constraints here included consumer prior knowledge, familiarity, agreement to material function and interest. Some of the bio-physical attributes were not selected due to the limited mentality. Four attributes were found to be the desired selections for optimal design of wet moss. For the semi-dry moss, there were 14. These attributes were validated successfully using a different consumer segment with minimum error. The desired attributes for the optimal design can be selected using consumer importance and its mentality constraints.

Precision Irrigation for Sunagoke Moss Production using Intelligent Image Analysis

There are many methods for sensing water condition in Sunagoke moss Rachomitrium canescens. The direct measurement of canopy is considered to be relatively inefficient and destructive to the plant. One alternative is the use of indirect measurement and non-destructive techniques such as machine vision. This study investigated the use of machine vision for monitoring water content in Sunagoke moss. The goal of this paper was to propose and investigate a combined genetic-neural algorithm to find the most significant image features or the sets of image features suitable for predicting Sunagoke moss water content. We extracted 50 features consisting of color, textural (Gray Level Co-occurrence Matrix and RGB Color Co-occurrence Matrix textural features) and morphological features. Ten textural features were calculated, including 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 feature but several associations of features that may be involved in determining water content. The genetic algorithm was able to select features with 27 selected features and artificial neural network was able to predict water content according to the selected features with minimum error of MSE 0.0021.

Development of Kansei-based Intelligent Decision Support System (KIDSS) for Quality Evaluation of a Biological Greening Material: Abstractive Parameters for Eliciting Consumer Satisfaction

Consumer satisfaction is one of the essential factors in quality evaluation of a biological greening material. It is relatively difficult to be elicited. This paper presents development of Kansei-based Intelligent Decision Support System (KIDSS) to elicit satisfaction. Elicitation was proposed by quantifying design attributes into Weighted Average Importance Index (WAII) and Consistency Index (CI). Attributes were selected using questionnaires and quantified using Kansei fitness function. The function was subsequently optimized by particle swarm optimization. As a case study, KIDSS was demonstrated on Sunagoke moss (Rhacomitrium japonicum). The result successfully indicated consistency of WAII and CI values regarding robustness. KIDSS was effectively validated in different consumer segments. WAII and CI are possible abstractive parameters to elicit satisfaction for quality evaluation.