DYNA

Apolo-Apolo, O.E., Martínez-Guanter, J., Egea, G., Raja, P., and Pérez-Ruiz, M., Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy, 115, art. 126030, 2020. DOI: https://doi.org/10.1016/j.eja.2020.126030 DOI: https://doi.org/10.1016/j.eja.2020.126030

Campobello, G., and Segreto, A., A low complexity image compression algorithm for IoT multimedia applications, 2019, 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, 2019, pp. 1-5. DOI: https://doi.org/10.23919/EUSIPCO.2019.8902678 DOI: https://doi.org/10.23919/EUSIPCO.2019.8902678

Carlsson, A., Kuzminykh, I., Franksson, R., and Liljegren, Measuring a LoRa network: performance, possibilities and limitations. In: Galinina, O., Andreev, S., Balandin, S., and Koucheryavy, Y., (eds). Internet of things, smart spaces, and next generation networks and systems. NEW2AN ruSMART 2018. Lecture Notes in Computer Science, vol 11118. Springer, Cham. 2018. DOI: https://doi.org/10.1007/978-3-030-01168-0_11 DOI: https://doi.org/10.1007/978-3-030-01168-0_11

Codeluppi, G., Cilfone, A., Davoli, L., and Ferrari, G., LoraFarM: a LoRaWAN-based smart farming modular IoT architecture. Sensors (Switzerland), 20(7), art. 2028, 2020. DOI: https://doi.org/10.3390/s20072028 DOI: https://doi.org/10.3390/s20072028

De Luna, R.G.R.G., Dadios, E.P.E.P., Bandala, A.A.A.A., and Vicerra, R.R.P.R.R.P., Tomato growth stage monitoring for smart farm using deep transfer learning with machine learning-based maturity grading. Agrivita, 42(1), pp. 24–36. 2020. DOI: https://doi.org/10.17503/agrivita.v42i1.2499 DOI: https://doi.org/10.17503/agrivita.v42i1.2499

Elijah, O., Rahman, T.A., Orikumhi, I., Leow, C.Y., and Hindia, M.N., An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet of Things Journal, 5(5), pp. 3758–3773, 2018. DOI: https://doi.org/10.1109/JIOT.2018.2844296 DOI: https://doi.org/10.1109/JIOT.2018.2844296

Feng, W., Ju, W., Li, A., Bao, W., and Zhang, J., High-Efficiency progressive transmission and automatic recognition of wildlife monitoring images with WISNs. IEEE Access, 7, pp. 161412–161423, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2951596 DOI: https://doi.org/10.1109/ACCESS.2019.2951596

Islam, N., Ray, B., and Pasandideh, F., IoT based smart farming: are the LPWAN technologies suitable for remote communication? Proceedings - 2020 IEEE International Conference on Smart Internet of Things, SmartIoT 2020, pp. 270–276, 2020. DOI: https://doi.org/10.1109/SmartIoT49966.2020.00048 DOI: https://doi.org/10.1109/SmartIoT49966.2020.00048

Jankowski, M., Gündüz, D., and Mikolajczyk, K., Deep joint transmission-recognition for power-constrained Iot devices. ArXiv, pp. 1–10, 2020.

Ji, M., Yoon, J., Choo, J., Jang, M., and Smith, A., LoRa-based visual monitoring scheme for agriculture IoT. SAS 2019 - 2019 IEEE Sensors Applications Symposium, Conference Proceedings. 2019. DOI: https://doi.org/10.1109/SAS.2019.8706100 DOI: https://doi.org/10.1109/SAS.2019.8706100

Kamilaris, A., Kartakoullis, A., and Prenafeta-Boldú, F.X., A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143, pp. 23–37, 2017. DOI: https://doi.org/10.1016/j.compag.2017.09.037 DOI: https://doi.org/10.1016/j.compag.2017.09.037

Khanna, A., and Kaur, S., Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture, 157(December 2018), pp. 218–231, 2019. DOI: https://doi.org/10.1016/j.compag.2018.12.039 DOI: https://doi.org/10.1016/j.compag.2018.12.039

Koc-San, D., Selim, S., Aslan, N., and San, B.T., Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform. Computers and Electronics in Agriculture, 150(February), pp. 289–301, 2018. DOI: https://doi.org/10.1016/j.compag.2018.05.001 DOI: https://doi.org/10.1016/j.compag.2018.05.001

Lee, S.W., and Kim, H.Y., An energy-efficient low-memory image compression system for multimedia IoT products. Eurasip Journal on Image and Video Processing, 2018(87), 2018. DOI: https://doi.org/10.1186/s13640-018-0333-3 DOI: https://doi.org/10.1186/s13640-018-0333-3

Liu, Z., Liu, T., Wen, W., Jiang, L., Xu, J., Wang, Y., and Quan, G., DeepN-JPEG: A deep neural network favorable JPEG-based image compression framework. Proceedings - Design Automation Conference, Part F1377(3), pp. 1–6, 2018. DOI: https://doi.org/10.1145/3195970.3196022 DOI: https://doi.org/10.1145/3195970.3196022

Lu, Y., Industry 4.0: a survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, pp. 1–10, 2017. DOI: https://doi.org/10.1016/j.jii.2017.04.005 DOI: https://doi.org/10.1016/j.jii.2017.04.005

Mekki, K., Bajic, E., Chaxel, F., and Meyer, F., Overview of cellular LPWAN Technologies for IoT deployment: Sigfox, LoRaWAN, and NB-IoT, in: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, 2018, pp. 197-202. DOI: https://doi.org/10.1109/PERCOMW.2018.8480255 DOI: https://doi.org/10.1109/PERCOMW.2018.8480255

Pathan, T.U., and Chakole, S., Sensor based smart farming and plant diseases monitoring. International Journal of Engineering and Advanced Technology, 8(2), pp. 442–446, 2019.

Pham, X., and Stack, M., How data analytics is transforming agriculture. Business Horizons, 61(1), pp. 125–133, 2018. DOI: https://doi.org/10.1016/j.bushor.2017.09.011 DOI: https://doi.org/10.1016/j.bushor.2017.09.011

Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., and Siegwart, R., WeedNet: dense semantic weed classification using multispectral images and MAV for smart farming. IEEE Robotics and Automation Letters, 3(1), pp. 588–595, 2018. DOI: https://doi.org/10.1109/LRA.2017.2774979 DOI: https://doi.org/10.1109/LRA.2017.2774979

Terence, S., and Purushothaman, G., Systematic review of Internet of Things in smart farming. Transactions on Emerging Telecommunications Technologies, 31(6), art. 3958, 2020. DOI: https://doi.org/10.1002/ett.3958 DOI: https://doi.org/10.1002/ett.3958

Tian, H., Wang, T., Liu, Y., Qiao, X., and Li, Y., Computer vision technology in agricultural automation —A review. Information Processing in Agriculture, 7(1), pp. 1–19, 2020. DOI: https://doi.org/10.1016/j.inpa.2019.09.006 DOI: https://doi.org/10.1016/j.inpa.2019.09.006

Yalcin, H., Phenology recognition using deep learning: DeepPheno. In: 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, pp. 1–4. DOI: https://doi.org/10.1109/SIU.2018.8404165 DOI: https://doi.org/10.1109/SIU.2018.8404165

Zhong, Y., Gao, J., Lei, Q., and Zhou, Y., A vision-based counting and recognition system for flying insects in intelligent agriculture. Sensors (Switzerland), 18(5), art. 1489, 2018. DOI: https://doi.org/10.3390/s18051489 DOI: https://doi.org/10.3390/s18051489