• Journal of agricultural medicine and community health
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• v.23 no.2
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• pp.259-268
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• 1998

It has been reported that among mushroom farmers, respiratory diseases such as hypersensitivity pneumonitis can be developed by inhalation of mushroom (Pleurotus ostreatus) spores. For the evaluation of respiratory symptoms among mushroom farmers, a questionnaire was made. The Questionnaire included general characteristics, past occupational histories, durations of the mushroom cultivation and characteristics of the symptoms. Following the questionnaire we interviewed the 72 mushroom farmers (12 males, 10 females) from Kyungbuk Province. We also examined white blood counts, erythrocyte sedimentation rates, eosinophil counts and total IgE counts for the 5 farmers. The results obtained are as follows. 1. The mean age among the 22 mushroom farmers was 46.9 years, and the mean duration of cultivation was 7.5 years. 2. Among the 22 mushroom farmers. 18 farmers (81.8 %) suffered from respiratory symptoms at work. The main symptom was coughing (100.0%), followed by chilling sensation (50.0 %), sputum (38.9 %) and sore throat (27.8 %). 3. Coughing and other associated symptoms occurred during work and disappeared after work or several days later when the exposure had been stopped. 4. Total IgE counts were elevated in all farmers examined the test. With above results, the respiratory symptoms that developed among mushroom farmers were typical patterns of hypersensitivity pneumonitis. Epidemiological studies and preventive measures for mushroom farmers should be established.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.