<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING"^^ . "Precision agriculture has been considered as one of the key components of the digital \r\ntransformation in Vietnam. In the view of information and communications technology, five major \r\ncases, including smart-crop monitoring, drone farming, smart-livestock monitoring, autonomous \r\nfarming machinery, and smart-building and -equipment management have the potential to radically \r\ntransform many aspects of agriculture. However, the establishment of digital tools used in smart \r\nagriculture programs has been still lacking. Of our interest, we reported two out of many cases of \r\nInternet-of-Things- (IoT-) based tools for the research in agriculture. In the first case, we investigated \r\nan electronic trap for automated monitoring of fall armyworm (FAW). Briefly, FAW (Spodoptera \r\nfrugiperda) has been reported as one of the most devastating pests that can attack maize (Zea mays) at \r\nall growth stages. Since the first occurrence of FAW in Vietnam has been reported in 2019, this \r\nlepidopteran pest had caused huge damage to maize production in the Northern provinces of Vietnam. \r\nThus, monitoring, identification, and management of FAW in the fields become one critical task for\r\nsustainable agricultural production. As the result, we introduced an automated FAW (adult moths) \r\ncounting system based on the traditional pheromone traps. The IoT sensors have been merged into the \r\ninstrument to count the frequency of adult moths and together record the temperature and humidity data. \r\nThe general data, including the real-time amount of trapped insects and environmental conditions, has \r\nbeen analyzed based on the machine-learning method, consequently, send to the Internet browser and \r\napplications. In the second case, we constructed a cost-effective phenotyping machine for automated \r\nseed imaging. Particularly, agronomists have an issue with the estimation of various typical \r\ncharacteristics of seeds, like length, width, mass, the color of the skin, and pubescence. By using the \r\ncomputer vision approach, we generated an easy-to-use tool for automatically measuring the general \r\nfeatures of crop seeds. The construction of this tool can significantly replace labors with only simple \r\noperations. Taken together, our tools could significantly provide a collection of digital tools for \r\nsupporting the digital transformation in research and development in the agriculture sector."^^ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Minh Trien"^^ . "Pham"^^ . "Minh Trien Pham"^^ . . "Quang Huy"^^ . "Vuong"^^ . "Quang Huy Vuong"^^ . . "Hong Linh"^^ . "Ta"^^ . "Hong Linh Ta"^^ . . "Viet Hong"^^ . "La"^^ . "Viet Hong La"^^ . . "Quoc Trung"^^ . "Nguyen"^^ . "Quoc Trung Nguyen"^^ . . "Van Hai"^^ . "Tong"^^ . "Van Hai Tong"^^ . . "Duc Ha"^^ . "Chu"^^ . "Duc Ha Chu"^^ . . "Minh Trung"^^ . "Vu"^^ . "Minh Trung Vu"^^ . . . . "Agricultural Biotechnology: Challenges and Opportunities"^^ . . . . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING (PDF)"^^ . . . "ky-yeu-ht_2.pdf"^^ . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING (Other)"^^ . . . . . . "preview.jpg"^^ . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING (Other)"^^ . . . . . . "medium.jpg"^^ . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING (Other)"^^ . . . . . . "small.jpg"^^ . . . "ESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #4696 \n\nESTABLISHMENT OF THE DIGITAL TOOLS FOR PRECISION AGRICULTURE BY MACHINE LEARNING\n\n" . "text/html" . . . "Agriculture Technology" . .