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AgriLoRa: A Digital Twin Framework for
Smart Agriculture 1Middle
East Technical University, Ankara,
06800 Turkey 2University
of Essex, Colchester, Essex, CO4 3SQ UK m.anisi@essex.ac.uk Abstract Throughout history, farmers and agricultural engineers have focused on the issue of increasing the yield of crops using different farming methods. In todayâs digitalized world, these techniques have been combined with IoT technology and machine learning algorithms, which have given rise to smart agriculture systems. However, farmers who live in developing countries hesitate to use such systems because of their hardware and maintenance costs. To address this issue, this paper proposes a lowcost farmland digital twin framework called AgriLoRa for smart agriculture. AgriLoRa consists of a wireless sensor network established in the farmland and cloud servers that run computer vision algorithms to detect plant diseases, weed clusters and plant nutrient deficiencies. In order to assess the feasibility of accurate plant disease detection, we have performed initial experiments with agricultural vision datasets using two different algorithms, the MobileNet and UNet models, and achieved successful results. AgriLoRa is promising to achieve a low-cost, high-precision smart agriculture solution to address the growing high-yield production needs of farmers worldwide. Keywords: smart agriculture, digital twins,
wireless sensor networks +: Corresponding author: Pelin Angin Journal of Wireless Mobile Networks, Ubiquitous
Computing, and Dependable Applications (JoWUA), Vol. 11, No.
4, pp. 77-96, December 2020 [pdf] DOI: 10.22667/JOWUA.2020.12.31.077 |