Abstract
Precision agriculture is a rapidly growing field focused on optimizing crop production while minimizing environmental impact. One promising approach involves the use of Deep Learning (DL), Image Processing (IP), and the Internet of Things (IoT) to detect crop diseases, pests, and nutrient deficiencies, allowing for targeted and precise application of treatments. Our study involved an analysis of 238 papers published between 2009 and March 2023.The findings reveal that IP and DL are the most frequently employed techniques in precision agriculture, primarily for detection purposes. The results have demonstrated a significant increase in crop yields and improved profitability for farmers with the adoption of these technologies. The overall integration of DL, IP, and IoT in precision agriculture holds immense potential to transform the agricultural industry and make it more sustainable and efficient.
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Alahiane, A., Asnaoui, K.E., Chadli, S., Saber, M. (2024). Systematic Mapping Study on the Use of Deep Learning, Image Processing, and IoT in Precision Agriculture. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-54318-0_15
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DOI: https://doi.org/10.1007/978-3-031-54318-0_15
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