eprintid: 673 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/00/06/73 datestamp: 2022-05-13 23:55:10 lastmod: 2023-07-11 23:30:11 status_changed: 2022-05-13 23:55:10 type: article metadata_visibility: show creators_name: Garg, Garima creators_name: Gupta, Shivam creators_name: Mishra, Preeti creators_name: Vidyarthi, Ankit creators_name: Singh, Aman creators_name: Ali, Asmaa creators_id: creators_id: creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: title: CROPCARE: An Intelligent Real-Time Sustainable IoT System for Crop Disease Detection Using Mobile Vision ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: none keywords: Smart Farming, Sustainable Computing, Internet-of-Agriculture-Things, Crop Analysis, Recommendation system abstract: Agriculture is an important sector that plays an essential role in the economic development of a country. Each year farmers face numerous challenges in producing good quality crops. One of the major reasons behind the failure of the harvest is the use of unscientific agricultural practices. Moreover, every year enormous crop loss is encountered either by pests, specific diseases, or natural disasters. It raises a strong concern to employ sustainable advanced technologies to address agriculture-related issues. In this paper, a sustainable real-time crop disease detection and prevention system, called CROPCARE is proposed. The system integrates mobile vision, Internet of Things (IoT), and Google Cloud services for sustainable growth of crops. The primary function of the proposed intelligent system is to detect crop diseases through the CROPCARE -mobile application. It uses Super-Resolution Convolution Network (SRCNN) and the pretrained model MobileNet-V2 to generate a decision model trained over various diseases. To maintain sustainability, the mobile app is integrated with IoT sensors and Google Cloud services. The proposed system also provides recommendations that help farmers know about current soil conditions, weather conditions, disease prevention methods, etc. It supports both Hindi and English dictionaries for the convenience of the farmers. The proposed approach is validated by using the PlantVillage dataset. The obtained results confirm the performance strength of the proposed system. date: 2023-02 publication: IEEE Internet of Things Journal pagerange: 1-1 id_number: doi:10.1109/JIOT.2021.3109019 refereed: TRUE issn: 2372-2541 official_url: http://doi.org/10.1109/JIOT.2021.3109019 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Cerrado Inglés Agriculture is an important sector that plays an essential role in the economic development of a country. Each year farmers face numerous challenges in producing good quality crops. One of the major reasons behind the failure of the harvest is the use of unscientific agricultural practices. Moreover, every year enormous crop loss is encountered either by pests, specific diseases, or natural disasters. It raises a strong concern to employ sustainable advanced technologies to address agriculture-related issues. In this paper, a sustainable real-time crop disease detection and prevention system, called CROPCARE is proposed. The system integrates mobile vision, Internet of Things (IoT), and Google Cloud services for sustainable growth of crops. The primary function of the proposed intelligent system is to detect crop diseases through the CROPCARE -mobile application. It uses Super-Resolution Convolution Network (SRCNN) and the pretrained model MobileNet-V2 to generate a decision model trained over various diseases. To maintain sustainability, the mobile app is integrated with IoT sensors and Google Cloud services. The proposed system also provides recommendations that help farmers know about current soil conditions, weather conditions, disease prevention methods, etc. It supports both Hindi and English dictionaries for the convenience of the farmers. The proposed approach is validated by using the PlantVillage dataset. The obtained results confirm the performance strength of the proposed system. metadata Garg, Garima; Gupta, Shivam; Mishra, Preeti; Vidyarthi, Ankit; Singh, Aman y Ali, Asmaa mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR (2023) CROPCARE: An Intelligent Real-Time Sustainable IoT System for Crop Disease Detection Using Mobile Vision. IEEE Internet of Things Journal. p. 1. ISSN 2372-2541