The current social demand for organic, sustainable, and eco-friendly approaches for farming, while ensuring the health and productivity of crops is increasing rapidly. Biocontrol agents are applied to crops to ensure biological control of plant pathogens. Research on the biological control of white root rot disease caused by a soil-borne pathogen, Rosellinia necatrix, is limited in pears compared to that in apple and avocado. This pathogenic fungus has an extensive host range, and symptoms of this disease include rotting of roots, yellowing and falling of leaves, wilting, and finally tree death. The severity of the disease caused by R. necatrix , makes it the most harmful fungal pathogen infecting the economical fruit tree species, such as pears, and is one of the main limiting factors in pear farming, with devastating effects on plant health and yield. In addition to agronomic and cultural practices, growers use chemical treatments to control the disease. However, rising public concern about environmental pollution and harmful effects of chemicals in humans and animals has facilitated the search for novel and environmentally friendly disease control methods. This review will briefly summarize the current status of biocontrol agents, ecofriendly methods, and possible approaches to control disease in pear orchards.
Since the emergence of deep learning-based chatbots for knowledge services, numerous research and development projects have been conducted in various industries. A high demand for chatbots has drastically increased the global market size; however, the limited functional scalability of open-domain chatbots is a challenge to their application to industries. Moreover, as most chatbot frameworks employ English, it is necessary to create chatbots customized for other languages. To address this problem, this paper proposes KoRASA as a pipeline-optimization method, which uses a deep learning-based open-source chatbot framework to understand the Korean language. KoRASA is a closed-domain chatbot that is applicable across a wide range of industries in Korea. KoRASA’s operation consists of four stages: tokenization, featurization, intent classification, and entity extraction. The accuracy and F1-score of KoRASA were measured based on datasets taken from common tasks carried out in most industrial fields. The algorithm for intent classification and entity extraction was optimized. The accuracy and F1-score were 98.2% and 98.4% for intent classification and 97.4% and 94.7% for entity extraction, respectively. Furthermore, these results are better than those achieved by existing models. Accordingly, KoRASA can be applied to various industries, including mobile services based on closed-domain chatbots using Korean, robotic process automation (RPA), edge computing, and Internet of Energy (IoE) services.
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