A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61440-8_12
Reference30 articles.
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2. Aeberli A, Phinn S, Johansen K, Robson A, Lamb DW (2023) Characterisation of banana plant growth using high-spatiotemporal-resolution multispectral UAV imagery. Remote Sens 15(3):679. https://doi.org/10.3390/rs15030679
3. Banerjee D, Kukreja V, Hariharan S, Sharma V (2023) Precision agriculture: classifying banana leaf diseases with hybrid deep learning models. In: 2023 IEEE 8th international conference for convergence in technology (I2CT). Lonavla, India. IEEE, pp 1–5. https://doi.org/10.1109/I2CT57861.2023.10126431
4. Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: a comprehensive updated review. Sensors 21(11):3758. https://doi.org/10.3390/s21113758
5. Calou VBC, Teixeira ADS, Moreira LCJ, Lima CS, De Oliveira JB, De Oliveira MRR (2020) The use of UAVs in monitoring yellow sigatoka in banana. Biosyst Eng 193:115–125. https://doi.org/10.1016/j.biosystemseng.2020.02.016
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