COMPUTER VISION IN PRECISION AGRICULTURE FOR WEED CONTROL: A SYSTEMATIC LITERATURE REVIEW
Yıl 2023,
, 581 - 600, 01.06.2023
Damla Karagozlu
,
John Karima Macharıa
Tolgay Karanfiller
Öz
The paper aims to carry out a systematic literature review to determine what computer vision techniques are prevalent in the field of precision agriculture, specifically for weed control. The review also noted what situations the techniques were best suited to and compared their various efficacy rates. The review covered a period between the years 2011 to 2022. The study findings indicate that computer vision in conjunction with machine learning and particularly Convolutional Neural Networks were the preferred options for most researchers. The techniques were generally applicable to all situations farmers may face themselves with a few exceptions, and they showed high efficacy rates across the board when it came to weed detection and control.
Kaynakça
- [1] L. C. Junior and J. Alfredo C. Ulson, “Real time weed detection using computer vision and Deep Learning,” 2021 14th IEEE International Conference on Industry Applications (INDUSCON), 2021. doi:10.1109/induscon51756.2021.9529761
- [2] O. C. Ghergan, D. Drăghiescu, I. Iosim, and P. A. Necşa “The Role of Computer Vision in Sustainable Agriculture,” 2021. Agricultural Management / Lucrari Stiintifice Seria I, Management Agricol, 23(2), 82-88.
- [3] M. Sonka, R. Boyle, and Vaclav Hlavac, Image processing, analysis, and machine vision. Andover] Cengage Learning, 2015.
- [4] W. Zhao, X. Wang, B. Qi, and T. Runge, “Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision,” IEEE Access, vol. 8, pp. 221975–221985, 2020, doi: https://doi.org/10.1109/access.2020.3043662.
- [5] A. M. S, Anju Anu Jose, C Bhuvanendran, D. Thomas, and Deepa Elizabeth George, “Farm-Copter: Computer Vision Based Precision Agriculture,” Sep. 2020, doi: https://doi.org/10.1109/icccsp49186.2020.9315239.
- [6] B. Kitchenham, “Procedures for Performing Systematic Reviews, Version 1.0,” Empir. Softw. Eng., vol. 33, no. 2004, pp. 1–26, 2004,[Online]. Available: https://www.researchgate.net/profile/Barbara-Kitchenham/publication/228756057_Procedures_for_Performing_Systematic_Reviews/links/618cfae961f09877207f8471/Procedures-for-Performing-Systematic-Reviews.pdf
- [7] J. You, W. Liu, and J. Lee, “A DNN-based semantic segmentation for detecting weed and crop,” Computers and Electronics in Agriculture, vol. 178, p. 105750, Nov. 2020, doi: https://doi.org/10.1016/j.compag.2020.105750.
- [8] M. H. Asad and A. Bais, “Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network,” Information Processing in Agriculture, vol. 7, no. 4, pp. 535–545, Dec. 2019, doi: https://doi.org/10.1016/j.inpa.2019.12.002.
- [9] S. G. Sodjinou, V. Mohammadi, A. T. Sanda Mahama, and P. Gouton, “A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images,” Information Processing in Agriculture, vol. 9, no. 3, Aug. 2021, doi: https://doi.org/10.1016/j.inpa.2021.08.003.
- [10] A. Wang, Y. Xu, X. Wei, and B. Cui, “Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination,” IEEE Access, vol. 8, pp. 81724–81734, 2020, doi: https://doi.org/10.1109/access.2020.2991354.
- [11] S. G c, Y. Zhang, C. Koparan, M. R. Ahmed, K. Howatt, and X. Sun, “Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions,” Journal of Agriculture and Food Research, vol. 9, p. 100325, Sep. 2022, doi: https://doi.org/10.1016/j.jafr.2022.100325.
- [12] K. N. Bhanu, T. B. Reddy, and M. Hanumanthappa, “Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks,” Egyptian Informatics Journal, vol. 20, no. 1, pp. 33–44, Mar. 2019, doi: https://doi.org/10.1016/j.eij.2018.07.001.
- [13] V. Partel, L. Costa, and Y. Ampatzidis, “Smart tree crop sprayer utilizing sensor fusion and artificial intelligence,” Computers and Electronics in Agriculture, vol. 191, p. 106556, Dec. 2021, doi: https://doi.org/10.1016/j.compag.2021.106556.
- [14] R. Kamath, M. Balachandra, and S. Prabhu, “Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study,” IEEE Access, vol. 7, pp. 45110–45122, 2019, doi: https://doi.org/10.1109/access.2019.2908846.
- [15] S. Cubero, E. Marco-Noales, N. Aleixos, S. Barbé, and J. Blasco, “RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing,” Agriculture, vol. 10, no. 7, p. 276, Jul. 2020, doi: https://doi.org/10.3390/agriculture10070276.
- [16] K. Dimililer and E. Kiani, “Application of back propagation neural networks on maize plant detection,” Procedia Computer Science, vol. 120, pp. 376–381, 2017, doi: https://doi.org/10.1016/j.procs.2017.11.253.
- [17] A. Albanese, M. Nardello, and D. Brunelli, “Automated Pest Detection With DNN on the Edge for Precision Agriculture,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 458–467, Sep. 2021, doi: https://doi.org/10.1109/jetcas.2021.3101740.
- [18] L. N. Smith, W. Zhang, M. F. Hansen, I. J. Hales, and M. L. Smith, “Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field,” Computers in Industry, vol. 97, pp. 122–131, May 2018, doi: https://doi.org/10.1016/j.compind.2018.02.002.
- [19] P. W. Khan, G. Xu, M. A. Latif, K. Abbas, and A. Yasin, “UAV’s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra,” IEEE Access, vol. 7, pp. 38442–38450, 2019, doi: https://doi.org/10.1109/access.2019.2906033.
- [20] A. del-Campo-Sanchez, R. Ballesteros, D. Hernandez-Lopez, J. F. Ortega, and M. A. Moreno, “Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques,” PLOS ONE, vol. 14, no. 4, p. e0215521, Apr. 2019, doi: https://doi.org/10.1371/journal.pone.0215521.
- [21] W.-H. Su, Ji Sheng Ma, and Q. Huang, “Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds,” Agriculture, vol. 12, no. 2, pp. 195–195, Jan. 2022, doi: https://doi.org/10.3390/agriculture12020195.
- [22] . E. Kiani, M. A. Shahadat, F. Sadikoglu, “Child perception-based plant species identification” Procedia Computer Science, vol 120, pp 357-364, 2017, https://doi.org/10.1016/j.procs.2017.11.250
- [23] S. Shorewala, A. Ashfaque, R. Sidharth, and U. Verma, “Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning,” IEEE Access, vol. 9, pp. 27971–27986, 2021, doi: https://doi.org/10.1109/access.2021.3057912.
- [24] F. Dankhara, K. Patel, and N. Doshi, “Analysis of robust weed detection techniques based on the Internet of Things (IoT),” Procedia Computer Science, vol. 160, pp. 696–701, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.025.
- [25] X. Li, R. Lloyd, S. Ward, J. Cox, S. Coutts, and C. Fox, “Robotic crop row tracking around weeds using cereal-specific features,” Computers and Electronics in Agriculture, vol. 197, p. 106941, Jun. 2022, doi: https://doi.org/10.1016/j.compag.2022.106941.
- [26] U. B. Patayon and R. V. Crisostomo, “Automatic Identification of Abaca Bunchy Top Disease using Deep Learning Models,” Procedia Computer Science, vol. 179, pp. 321–329, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.012.
- [27] H. Liu and J. S. Chahl, “Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images,” Artificial Intelligence in Agriculture, vol. 5, pp. 13–23, 2021, doi: https://doi.org/10.1016/j.aiia.2021.01.003.
- [28] V. Partel, S. Charan Kakarla, and Y. Ampatzidis, “Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence,” Computers and Electronics in Agriculture, vol. 157, pp. 339–350, Feb. 2019, doi: https://doi.org/10.1016/j.compag.2018.12.048.
- [29] C. Hu, B. B. Sapkota, J. A. Thomasson, and M. V. Bagavathiannan, “Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping,” Remote Sensing, vol. 13, no. 11, p. 2140, May 2021, doi: https://doi.org/10.3390/rs13112140.
- [30] R. Kamath, M. Balachandra, and S. Prabhu, “Crop and weed discrimination using Laws’ texture masks,” International Journal of Agricultural and Biological Engineering, vol. 13, no. 1, pp. 191–197, 2020, doi: https://doi.org/10.25165/j.ijabe.20201301.4920.
- [31] V. N. T. Le, S. Ahderom, B. Apopei, and K. Alameh, “A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators,” GigaScience, vol. 9, no. 3, Mar. 2020, doi: https://doi.org/10.1093/gigascience/giaa017.
- [32] S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,” Procedia Computer Science, vol. 167, pp. 2003–2010, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.236.
- [33] P. Varalakshmi and S. Aravindkumar, “Plant disorder precognition by image based pattern recognition,” Procedia Computer Science, vol. 165, pp. 502–510, 2019, doi: https://doi.org/10.1016/j.procs.2020.01.018.
- [34] U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, “Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease,” IEEE Access, vol. 7, pp. 43721–43729, 2019, doi: https://doi.org/10.1109/access.2019.2907383.
- [35] F. Pallottino et al., “Machine Vision Retrofit System for Mechanical Weed Control in Precision Agriculture Applications,” Sustainability, vol. 10, no. 7, p. 2209, Jun. 2018, doi: https://doi.org/10.3390/su10072209.
- [36] P. Bosilj, T. Duckett, and G. Cielniak, “Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture,” Computers in Industry, vol. 98, pp. 226–240, Jun. 2018, doi: https://doi.org/10.1016/j.compind.2018.02.003.
- [37] E. Kiani and T. A. Mamedov, “Identification of plant disease infection using soft-computing: Application to modern botany,” Procedia Computer Science, vol. 120, pp. 893–900, Jan. 2017, doi: https://doi.org/10.1016/j.procs.2017.11.323.
- [38] M. Montalvo et al., “Automatic detection of crop rows in maize fields with high weeds pressure,” Expert Systems with Applications, vol. 39, no. 15, pp. 11889–11897, Nov. 2012, doi: https://doi.org/10.1016/j.eswa.2012.02.117.
- [39] X. P. Burgos-Artizzu, A. Ribeiro, M. Guijarro, and G. Pajares, “Real-time image processing for crop/weed discrimination in maize fields,” Computers and Electronics in Agriculture, vol. 75, no. 2, pp. 337–346, Feb. 2011, doi: https://doi.org/10.1016/j.compag.2010.12.011.
- [40] A. Tellaeche, G. Pajares, X. P. Burgos-Artizzu, and A. Ribeiro, “A computer vision approach for weeds identification through Support Vector Machines,” Applied Soft Computing, vol. 11, no. 1, pp. 908–915, Jan. 2011, doi: https://doi.org/10.1016/j.asoc.2010.01.011.
COMPUTER VISION IN PRECISION AGRICULTURE FOR WEED CONTROL: A SYSTEMATIC LITERATURE REVIEW
Yıl 2023,
, 581 - 600, 01.06.2023
Damla Karagozlu
,
John Karima Macharıa
Tolgay Karanfiller
Öz
Bu çalışma, hassas tarım alanında yabancı ot kontrolü için yaygın kullanılan bilgisayarlı görme tekniklerini ortaya koymak amacıyla sistematik literatür taraması yapmayı amaçlamaktadır. Gerçekleştirilen literatür incelemesinde ayrıca bilgisayarlı görme tekniklerinin verimli olduğu durumlar da açıklanmaktadır. Çalışma kapsamını 2011 ile 2022 yılları arasındaki literatür çalışmaları oluşturmaktadır. Çalışma bulguları, makine öğrenimi ve özellikle Konvolüsyonel Sinir Ağları ile birlikte bilgisayarlı görmenin birçok araştırmacı tarafından tercih edilen seçenekler olduğunu göstermektedir. Bilgisayarlı görme tekniklerinin genel olarak çiftçilerin karşılaşabilecekleri tüm durumlara uygulanabilirliği, yabancı ot tespiti ve kontrolü konularında yüksek etkinlik oranları gösterdiği sonuçları elde edilmiştir.
Kaynakça
- [1] L. C. Junior and J. Alfredo C. Ulson, “Real time weed detection using computer vision and Deep Learning,” 2021 14th IEEE International Conference on Industry Applications (INDUSCON), 2021. doi:10.1109/induscon51756.2021.9529761
- [2] O. C. Ghergan, D. Drăghiescu, I. Iosim, and P. A. Necşa “The Role of Computer Vision in Sustainable Agriculture,” 2021. Agricultural Management / Lucrari Stiintifice Seria I, Management Agricol, 23(2), 82-88.
- [3] M. Sonka, R. Boyle, and Vaclav Hlavac, Image processing, analysis, and machine vision. Andover] Cengage Learning, 2015.
- [4] W. Zhao, X. Wang, B. Qi, and T. Runge, “Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision,” IEEE Access, vol. 8, pp. 221975–221985, 2020, doi: https://doi.org/10.1109/access.2020.3043662.
- [5] A. M. S, Anju Anu Jose, C Bhuvanendran, D. Thomas, and Deepa Elizabeth George, “Farm-Copter: Computer Vision Based Precision Agriculture,” Sep. 2020, doi: https://doi.org/10.1109/icccsp49186.2020.9315239.
- [6] B. Kitchenham, “Procedures for Performing Systematic Reviews, Version 1.0,” Empir. Softw. Eng., vol. 33, no. 2004, pp. 1–26, 2004,[Online]. Available: https://www.researchgate.net/profile/Barbara-Kitchenham/publication/228756057_Procedures_for_Performing_Systematic_Reviews/links/618cfae961f09877207f8471/Procedures-for-Performing-Systematic-Reviews.pdf
- [7] J. You, W. Liu, and J. Lee, “A DNN-based semantic segmentation for detecting weed and crop,” Computers and Electronics in Agriculture, vol. 178, p. 105750, Nov. 2020, doi: https://doi.org/10.1016/j.compag.2020.105750.
- [8] M. H. Asad and A. Bais, “Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network,” Information Processing in Agriculture, vol. 7, no. 4, pp. 535–545, Dec. 2019, doi: https://doi.org/10.1016/j.inpa.2019.12.002.
- [9] S. G. Sodjinou, V. Mohammadi, A. T. Sanda Mahama, and P. Gouton, “A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images,” Information Processing in Agriculture, vol. 9, no. 3, Aug. 2021, doi: https://doi.org/10.1016/j.inpa.2021.08.003.
- [10] A. Wang, Y. Xu, X. Wei, and B. Cui, “Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination,” IEEE Access, vol. 8, pp. 81724–81734, 2020, doi: https://doi.org/10.1109/access.2020.2991354.
- [11] S. G c, Y. Zhang, C. Koparan, M. R. Ahmed, K. Howatt, and X. Sun, “Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions,” Journal of Agriculture and Food Research, vol. 9, p. 100325, Sep. 2022, doi: https://doi.org/10.1016/j.jafr.2022.100325.
- [12] K. N. Bhanu, T. B. Reddy, and M. Hanumanthappa, “Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks,” Egyptian Informatics Journal, vol. 20, no. 1, pp. 33–44, Mar. 2019, doi: https://doi.org/10.1016/j.eij.2018.07.001.
- [13] V. Partel, L. Costa, and Y. Ampatzidis, “Smart tree crop sprayer utilizing sensor fusion and artificial intelligence,” Computers and Electronics in Agriculture, vol. 191, p. 106556, Dec. 2021, doi: https://doi.org/10.1016/j.compag.2021.106556.
- [14] R. Kamath, M. Balachandra, and S. Prabhu, “Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study,” IEEE Access, vol. 7, pp. 45110–45122, 2019, doi: https://doi.org/10.1109/access.2019.2908846.
- [15] S. Cubero, E. Marco-Noales, N. Aleixos, S. Barbé, and J. Blasco, “RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing,” Agriculture, vol. 10, no. 7, p. 276, Jul. 2020, doi: https://doi.org/10.3390/agriculture10070276.
- [16] K. Dimililer and E. Kiani, “Application of back propagation neural networks on maize plant detection,” Procedia Computer Science, vol. 120, pp. 376–381, 2017, doi: https://doi.org/10.1016/j.procs.2017.11.253.
- [17] A. Albanese, M. Nardello, and D. Brunelli, “Automated Pest Detection With DNN on the Edge for Precision Agriculture,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 458–467, Sep. 2021, doi: https://doi.org/10.1109/jetcas.2021.3101740.
- [18] L. N. Smith, W. Zhang, M. F. Hansen, I. J. Hales, and M. L. Smith, “Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field,” Computers in Industry, vol. 97, pp. 122–131, May 2018, doi: https://doi.org/10.1016/j.compind.2018.02.002.
- [19] P. W. Khan, G. Xu, M. A. Latif, K. Abbas, and A. Yasin, “UAV’s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra,” IEEE Access, vol. 7, pp. 38442–38450, 2019, doi: https://doi.org/10.1109/access.2019.2906033.
- [20] A. del-Campo-Sanchez, R. Ballesteros, D. Hernandez-Lopez, J. F. Ortega, and M. A. Moreno, “Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques,” PLOS ONE, vol. 14, no. 4, p. e0215521, Apr. 2019, doi: https://doi.org/10.1371/journal.pone.0215521.
- [21] W.-H. Su, Ji Sheng Ma, and Q. Huang, “Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds,” Agriculture, vol. 12, no. 2, pp. 195–195, Jan. 2022, doi: https://doi.org/10.3390/agriculture12020195.
- [22] . E. Kiani, M. A. Shahadat, F. Sadikoglu, “Child perception-based plant species identification” Procedia Computer Science, vol 120, pp 357-364, 2017, https://doi.org/10.1016/j.procs.2017.11.250
- [23] S. Shorewala, A. Ashfaque, R. Sidharth, and U. Verma, “Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning,” IEEE Access, vol. 9, pp. 27971–27986, 2021, doi: https://doi.org/10.1109/access.2021.3057912.
- [24] F. Dankhara, K. Patel, and N. Doshi, “Analysis of robust weed detection techniques based on the Internet of Things (IoT),” Procedia Computer Science, vol. 160, pp. 696–701, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.025.
- [25] X. Li, R. Lloyd, S. Ward, J. Cox, S. Coutts, and C. Fox, “Robotic crop row tracking around weeds using cereal-specific features,” Computers and Electronics in Agriculture, vol. 197, p. 106941, Jun. 2022, doi: https://doi.org/10.1016/j.compag.2022.106941.
- [26] U. B. Patayon and R. V. Crisostomo, “Automatic Identification of Abaca Bunchy Top Disease using Deep Learning Models,” Procedia Computer Science, vol. 179, pp. 321–329, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.012.
- [27] H. Liu and J. S. Chahl, “Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images,” Artificial Intelligence in Agriculture, vol. 5, pp. 13–23, 2021, doi: https://doi.org/10.1016/j.aiia.2021.01.003.
- [28] V. Partel, S. Charan Kakarla, and Y. Ampatzidis, “Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence,” Computers and Electronics in Agriculture, vol. 157, pp. 339–350, Feb. 2019, doi: https://doi.org/10.1016/j.compag.2018.12.048.
- [29] C. Hu, B. B. Sapkota, J. A. Thomasson, and M. V. Bagavathiannan, “Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping,” Remote Sensing, vol. 13, no. 11, p. 2140, May 2021, doi: https://doi.org/10.3390/rs13112140.
- [30] R. Kamath, M. Balachandra, and S. Prabhu, “Crop and weed discrimination using Laws’ texture masks,” International Journal of Agricultural and Biological Engineering, vol. 13, no. 1, pp. 191–197, 2020, doi: https://doi.org/10.25165/j.ijabe.20201301.4920.
- [31] V. N. T. Le, S. Ahderom, B. Apopei, and K. Alameh, “A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators,” GigaScience, vol. 9, no. 3, Mar. 2020, doi: https://doi.org/10.1093/gigascience/giaa017.
- [32] S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,” Procedia Computer Science, vol. 167, pp. 2003–2010, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.236.
- [33] P. Varalakshmi and S. Aravindkumar, “Plant disorder precognition by image based pattern recognition,” Procedia Computer Science, vol. 165, pp. 502–510, 2019, doi: https://doi.org/10.1016/j.procs.2020.01.018.
- [34] U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, “Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease,” IEEE Access, vol. 7, pp. 43721–43729, 2019, doi: https://doi.org/10.1109/access.2019.2907383.
- [35] F. Pallottino et al., “Machine Vision Retrofit System for Mechanical Weed Control in Precision Agriculture Applications,” Sustainability, vol. 10, no. 7, p. 2209, Jun. 2018, doi: https://doi.org/10.3390/su10072209.
- [36] P. Bosilj, T. Duckett, and G. Cielniak, “Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture,” Computers in Industry, vol. 98, pp. 226–240, Jun. 2018, doi: https://doi.org/10.1016/j.compind.2018.02.003.
- [37] E. Kiani and T. A. Mamedov, “Identification of plant disease infection using soft-computing: Application to modern botany,” Procedia Computer Science, vol. 120, pp. 893–900, Jan. 2017, doi: https://doi.org/10.1016/j.procs.2017.11.323.
- [38] M. Montalvo et al., “Automatic detection of crop rows in maize fields with high weeds pressure,” Expert Systems with Applications, vol. 39, no. 15, pp. 11889–11897, Nov. 2012, doi: https://doi.org/10.1016/j.eswa.2012.02.117.
- [39] X. P. Burgos-Artizzu, A. Ribeiro, M. Guijarro, and G. Pajares, “Real-time image processing for crop/weed discrimination in maize fields,” Computers and Electronics in Agriculture, vol. 75, no. 2, pp. 337–346, Feb. 2011, doi: https://doi.org/10.1016/j.compag.2010.12.011.
- [40] A. Tellaeche, G. Pajares, X. P. Burgos-Artizzu, and A. Ribeiro, “A computer vision approach for weeds identification through Support Vector Machines,” Applied Soft Computing, vol. 11, no. 1, pp. 908–915, Jan. 2011, doi: https://doi.org/10.1016/j.asoc.2010.01.011.