RT Journal Article
T1 SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring
A1 Contreras Castillo, Juan
A1 Guerrero Ibañez, Juan Antonio
A1 Santana Mancilla, Pedro C.
A1 Anido Rifón, Luis Eulogio
K1 1203.04 Inteligencia Artificial
K1 3103 Agronomía
K1 3103.04 Protección de Los Cultivos
AB The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%.
PB Applied Sciences
SN 20763417
YR 2023
FD 2023-02-02
LK http://hdl.handle.net/11093/4487
UL http://hdl.handle.net/11093/4487
LA eng
NO Applied Sciences, 13(3): 1961 (2023)
DS Investigo
RD 04-dic-2024