Closed House Chicken Barn Climate Control Using Fuzzy Inference System

The hazardous gases in chicken barn such as Ammonia (NH3) and hydrogen sulfide (H2S) are the health threats to the farm animals and workers which influenced by climate changes. The chicken barn requires real-time control to maintain the barn climate and monitor hazardous gases. The outdated on-off and proportional control are not so efficient in energy saving and productivity. The solution to monitor environment of the chicken barn is using wireless electronic nose (e-nose) and Short Messaging System (SMS). The e-nose system is used for the barn’s temperature and humidity data acquisition. The chicken barn climate control is utilizing fuzzy interface system. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the climate control system. Results show that the performance of the fuzzy method can improve the system to control the barn’s climate. This system also provides real-time alerts to farmers based on specific limit value for the climate. It makes it easier for farmers to follow up on-site or remotely control the environmental conditions in the barn by using the SMS system

Electronic Nose Calibration Process for Monitoring Atmospheric Hazards in Confined Space Applications

Confined space is an enclosed area with limited space to perform work activity which could contribute towards atmospheric hazards accidents. The atmospheric air sample can be monitored using the integration of electronic nose (e-nose) together with mobile robot. In this work, we reported the calibration of e-nose which consists of three individual Metal Oxides Semi-Conductor (MOS) gas sensors together with oxygen, temperature and humidity sensors for environmental monitoring. The sample gas is using two different gas cylinders. Gas cylinder 1 contains of hydrogen sulphide (H2S), carbon monoxide (CO) and methane (CH4) while gas cylinder 2 contains air with zero grades. The analogue to digital converter (ADC) readings from the MOS gas sensors response is converted into parts per million (ppm) and percentage (%) readings. The concentrations of gas in cylinders were validated using commercial gas detector. The difference readings between the MOS gas sensors in e-nose and commercial gas detector to the gas cylinder 1 is calculated as calibrated value. The gas cylinder 2 exposed is to identify the ability of MOS gas sensors to back in baseline level. Results proved the ability of the developed e-nose to be use in environmental gas detections and monitoring

Image Processing Techniques for Harumanis Disease Severity and Weighting Estimation for Automatic Grading System Application

Harumanis Mango is known as the king of Mangoes. It is very nutritious and rich with carotenes. However, many of the farmers and agriculture experts reported that they have problems in grading and inspecting the Harumanis Mango. Sometimes, Mango production loses its quality due to diseases that are not even visible to the naked eyes. Traditionally, farmers and agriculture experts will estimate the severity of the disease using their experiences. While for weight estimation, manual inspection was done by using a weight scale. This traditional method has its own drawbacks as it can lead to some errors due to inconsistencies made by human inspection. Furthermore, they are less efficient and very time-consuming. Therefore, an automated procedure that able to classify the disease severities and weight estimations would be much appreciated. With the aid of image processing techniques, diseases can be classified according to its scale, and its weight can be estimated. A number of pixels of Harumanis Mango will be used for classification. The analysis will be done by using the statistical method of regression. It shows that the accuracy of weight estimation is 72.25%

Simulation of Robot Navigation for Hospital’s Confined Space Areas Using Fuzzy Control

The atmospheric hazards that may be present in confined space pose a serious threat to human while carrying special task in the respective location. Among the areas that are considered as confined space are utility tunnel, boiler, storage tank, sewage lines, utility holes and underground electrical fault. This study is concerned with hospital’s enclosed space areas which include mechanical room and medicine storage area. Currently, there is no specific technique to replace worker for a task such as navigation and collecting air sample in the confined space for monitoring the hazards. A mobile robot integrated with CCD camera can be used to manoeuvre through the environment and at the same time monitor the situation in confined space. This paper discusses simulation of robot navigation for hospital’s confined space areas. Fuzzy control was implemented to allow the robot to perform semiautonomous obstacle avoidance in the simulated environment. The result shows that the robot was able to manoeuvre around without any collision

Detection of Colletotrichum Gloeosporioides Fungus Isolates Development/Spread for Mango (Mangifera Indica L.) Cultivar from Electronic Nose Using Multivariate-Statistical Analysis

Agriculture plays a very important role in Asia economic sectors. For Malaysia, it plays a big contribution towards the country’s development. Mangifera Indica L., commonly known as Mango, is one of the fruit that has high economic demand and potential in Malaysia export business. However, due to radical climate changes from hot to humid, Mango is exposed towards a number of disease and this will affect its production. Colletotrichum gloeosporioides is one of the major diseases that could occur on any types of Mango. This fungus can attack on fruit skin and leaf, therefore a method that able to detect and control it would be much appreciated. Hence, this paper shows that the presence of Colletotrichum gloeosporioides type of pathogen can be detected by using Electronic Nose (E-Nose). The E-Nose will detect the Volatile Organic Compound (VOC) that produced from this fungus. Further analysis and justification on its existence are completed by using one of Multivariate-Statistical Analysis method which is Principal Component Analysis (PCA).The analysis results effectively show that the PCA is able to classify the number of isolating days of this type of fungus after cultured. Furthermore the potential of pre-symptomatic detection of the plant diseases was demonstrated