References
[1]. Ahmed, F., Al-Mamun, H. A., Bari, A. H., Hossain, E., &
Kwan, P. (2012). Classification of crops and weeds from
digital images: A support vector machine approach.
Crop Protection, 40, 98-104. https://doi.org/10.1016/j.cropro.2012.04.024
[2]. Alam, M., Alam, M. S., Roman, M., Tufail, M., Khan, M.
U., & Khan, M. T. (2020, April). Real-time machine-learning
based crop/weed detection and classification for
variable-rate spraying in precision agriculture. In 2020, 7th
International Conference on Electrical and Electronics
Engineering (ICEEE) (pp. 273-280). IEEE. https://doi.org/10.1109/ICEEE49618.2020.9102505
[3]. Bakhshipour, A., & Jafari, A. (2018). Evaluation of
support vector machine and artificial neural networks in
weed detection using shape features. Computers and
Electronics in Agriculture, 145, 153-160. https://doi.org/10.1016/j.compag.2017.12.032
[4]. Bhatkar, A. P., &Kharat, G. U. (2015, December).
Detection of diabetic retinopathy in retinal images using
MLP classifier. In 2015, IEEE International Symposium on
Nanoelectronic and Information Systems (pp. 331-335).
IEEE. https://doi.org/10.1109/iNIS.2015.30
[5]. Bini, D., Pamela, D., & Prince, S. (2020, March).
Machine vision and machine learning for intelligent
agrobots: A review. In 2020 5th International Conference
on Devices, Circuits and Systems (ICDCS) (pp. 12-16). IEEE.
https://doi.org/10.1109/ICDCS48716.2020.243538
[6]. Burkart, N., & Huber, M. F. (2021). A survey on the
explainability of supervised machine learning. Journal of
Artificial Intelligence Research, 70, 245-317. https://doi.org/10.1613/jair.1.12228
[7]. Chauhan, N. K., & Singh, K. (2018, September). A
review on conventional machine learning vs deep
learning. In 2018, International Conference on
Computing, Power and Communication Technologies (GUCON) (pp. 347-352). IEEE. https://doi.org/10.1109/GUCON.2018.8675097
[8]. Dokic, K., Blaskovic, L., &Mandusic, D. (2020,
December). From machine learning to deep learning in
agriculture–the quantitative review of trends. In IOP
Conference Series: Earth and Environmental Science,
614(1), 012138. IOP Publishing. https://doi.org/10.1088/1755-1315/614/1/012138
[9]. Géron, A. (2022). Hands-on machine learning with
Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc..
[10]. Hamuda, E., Glavin, M., & Jones, E. (2016). A survey
of image processing techniques for plant extraction and
segmentation in the field. Computers and Electronics in
Agriculture, 125, 184-199. https://doi.org/10.1016/j.compag.2016.04.024
[11]. Hemming, J., & Rath, T. (2001). PA—Precision
agriculture: Computer-vision-based weed identification
under field conditions using controlled lighting. Journal of
Agricultural Engineering Research, 78(3), 233-243.
https://doi.org/10.1006/jaer.2000.0639
[12]. Humeau-Heurtier, A. (2019). Texture feature
extraction methods: A survey. IEEE Access, 7, 8975-9000.
https://doi.org/10.1109/Access.2018.2890743
[13]. Islam, N., Rashid, M. M., Wibowo, S., Xu, C. Y.,
Morshed, A., Wasimi, S. A., ...& Rahman, S. M. (2021).
Early weed detection using image processing and
machine learning techniques in an Australian chilli farm.
Agriculture, 11(5), 387. https://doi.org/10.3390/agriculture11050387
[14]. Jabir, B., Falih, N., Sarih, A., & Tannouche, A. (2021).
A strategic analytics using convolutional neural networks
for weed identification in sugar beet fields. AGRIS on-line
Papers in Economics and Informatics, 13(665-2022-448),
49-57. https://doi.org/10.22004/ag.econ.320247
[15]. Júnior, P. C. P., Monteiro, A., Da Luz Ribeiro, R.,
Sobieranski, A. C., & Von Wangenheim, A. (2020).
Comparison of supervised classifiers and image features
for crop rows segmentation on aerial images. Applied
Artificial Intelligence, 34(4), 271-291. https://doi.org/10.1080/08839514.2020.1720131
[16]. Kazmi, W., Garcia-Ruiz, F., Nielsen, J., Rasmussen, J., & Andersen, H. J. (2015). Exploiting affine invariant regions
and leaf edge shapes for weed detection. Computers
and Electronics in Agriculture, 118, 290-299. https://doi.org/10.1016/j.compag.2015.08.023
[17]. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., &
Bochtis, D. (2018). Machine learning in agriculture: A
review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
[18]. Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood
prediction using machine learning models: Literature
review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
[19]. Sharma, P., Hans, P., & Gupta, S. C. (2020, January).
Classification of plant leaf diseases using machine
learning and image preprocessing techniques. In 2020
10th International Conference on Cloud Computing,
Data Science & Engineering (Confluence) (pp. 480-484).
IEEE. https://doi.org/10.1109/Confluence47617.2020.9057889
[20]. Tian, L., Slaughter, D. C., & Norris, R. F. (2000).
Machine vision identification of tomato seedlings for
automated weed control. Transactions of ASAE, 40(6),
1761-1768.
[21]. Wang, A., Zhang, W., & Wei, X. (2019). A review on
weed detection using ground-based machine vision and
image processing techniques. Computers and
Electronics in Agriculture, 158, 226-240. https://doi.org/10.1016/j.compag.2019.02.005
[22]. Wang, C., Wang, J., Du, Q., & Yang, X. (2020,
December). Dog breed classification based on deep
learning. In 2020 13th International Symposium on
Computational Intelligence and Design (ISCID) (pp. 209-212). IEEE. https://doi.org/10.1109/ISCID51228.2020.
00053
[23]. Wang, P., Fan, E., & Wang, P. (2021). Comparative
analysis of image classification algorithms based on
traditional machine learning and deep learning. Pattern
Recognition Letters, 141, 61-67. https://doi.org/10.1016/j.patrec.2020.07.042
[24]. Wendel, A., & Underwood, J. (2016, May). Selfsupervised
weed detection in vegetable crops using ground based hyperspectral imaging. In 2016, IEEE
International Conference on Robotics and Automation (ICRA) (pp. 5128-5135). IEEE. https://doi.org/10.1109/ICRA.2016.7487717
