Abstract
Crop diseases pose significant challenges to the agricultural industry, affecting crop health, yield, and economic stability. Traditional disease control methods are often labor-intensive and imprecise, highlighting the need for advanced technologies. The chapter focuses on the integration of computer vision and robotics for disease control in agriculture. Computer vision, with its ability to analyze visual data, offers promising solutions for disease identification and monitoring. By utilizing machine learning algorithms and image analysis techniques, computer vision systems can accurately detect and classify diseases, enabling early intervention and targeted treatments. The integration of computer vision with agricultural robotics further enhances disease control capabilities. Autonomous robots equipped with cameras and sensors can navigate fields, capturing high-resolution images for disease detection. These images, along with other data sources, can be analyzed using computer vision algorithms to monitor crop health and guide precise interventions. Decision support systems integrate the insights from computer vision and robotics, enabling data-driven decision-making for disease control. The chapter further discusses the fundamentals of computer vision, including image acquisition, preprocessing, and feature extraction techniques. It explores the application of computer vision in disease identification and classification, highlighting the benefits and challenges associated with these techniques. The role of robotics in disease control, including autonomous robots and targeted pesticide application, is also examined. Some case studies and success stories demonstrate the practical implementation and impact of computer vision and robotics in disease control. Additionally, it discusses the challenges, future directions, and emerging technologies in the field. Overall, the chapter underscores the transformative potential of computer vision and robotics in revolutionizing disease control practices in agriculture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):53. https://doi.org/10.1186/s40537-021-00444-8
Bonczek RH, Holsapple CW, Whinston AB (2014) Foundations of decision support systems. Academic Press
Boubakri A, Mettali Gamar S (2021) A new architecture of autonomous vehicles: redundant architecture to improve operational safety. Int J Robot Control Syst 1(3):355–368. https://doi.org/10.31763/ijrcs.v1i3.437
Challenges of Computer Vision Adoption in Agriculture and How to Solve Them | Mindy Support Outsourcing (n.d.) Mindy-Support.com. https://mindy-support.com/news-post/challenges-of-computer-vision-adoption-in-agriculture-and-how-to-solve-them/. Accessed 5 Aug 2023
Cheng C, Fu J, Su H, Ren L (2023) Recent advancements in agriculture robots: benefits and challenges. Mach Des 11(1):48. https://doi.org/10.3390/machines11010048
Chouhan SS, Kaul A, Singh UP (2019a) Radial basis function neural network for the segmentation of plant leaf disease. https://doi.org/10.1109/iscon47742.2019.9036299
Chouhan SS, Kaul A, Singh UP (2019d) A deep learning approach for the classification of diseased plant leaf images. In 2019 International conference on communication and electronics systems (ICCES) (pp 1168–1172). IEEE. https://doi.org/10.1109/ICCES45898.2019.9002201
Chouhan SS, Kaul A, Sinzlr UP (2019c) Plants leaf segmentation using bacterial foraging optimization algorithm. https://doi.org/10.1109/icces45898.2019.9002039
Chouhan SS, Singh UP, Jain S (2019b) Applications of computer vision in plant pathology: a survey. Arch Comput Methods Eng 27(2):611–632. https://doi.org/10.1007/s11831-019-09324-0
Chouhan SS, Singh UP, Jain S (2021b) Automated plant leaf disease detection and classification using fuzzy based function network. Wirel Pers Commun 121:1757–1779. https://doi.org/10.1007/s11277-021-08734-3
Chouhan SS, Singh UP, Sharma U, Jain S (2021a) Leaf disease segmentation and classification of Jatropha Curcas L. and Pongamia Pinnata L. biofuel plants using computer vision based approaches. Measurement 171:108796. https://doi.org/10.1016/j.measurement.2020.108796
Deshmukh D, Pratihar DK, Deb AK, Ray H, Bhattacharyya N (2021) Design and development of intelligent pesticide spraying system for agricultural robot. In: Hybrid intelligent systems: 20th international conference on hybrid intelligent systems (HIS 2020), December 14–16, 2020. Springer, pp 157–170
Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R (2021) Deep learning-enabled medical computer vision. NPJ Digit Med 4(1):5. https://doi.org/10.1038/s41746-020-00376-2
FAO (2021) FAO - News Article: Climate change fans spread of pests and threatens plants and crops, new FAO study. https://www.fao.org/news/story/en/item/1402920/icode/
Foglia MM, Reina G (2006) Agricultural robot for radicchio harvesting. J Field Robot 23(6–7):363–377. https://doi.org/10.1002/rob.20131
Fountas S, Malounas I, Athanasakos L, Avgoustakis I, Espejo-Garcia B (2022) AI-assisted vision for agricultural robots. AgriEngineering 4(3):674–694. https://doi.org/10.3390/agriengineering4030043
Fountas S, Mylonas N, Malounas I, Rodias E, Hellmann Santos C, Pekkeriet E (2020) Agricultural robotics for field operations. Sensors 20(9):2672. https://doi.org/10.3390/s20092672
Ghyar BS, Birajdar GK (2017) Computer vision based approach to detect rice leaf diseases using texture and color descriptors. IEEE Xplore. https://doi.org/10.1109/ICICI.2017.8365305
Gonzalez-de-Soto M, Emmi L, Perez-Ruiz M, Aguera J, Gonzalez-de-Santos P (2016) Autonomous systems for precise spraying – evaluation of a robotised patch sprayer. Biosyst Eng 146:165–182. https://doi.org/10.1016/j.biosystemseng.2015.12.018
Grinbergs H (2016) Production quality decision support using real-time computer vision framework. Proceedings of Eng. Rural Dev 5–27
Han L, Haleem MS, Taylor M (2015) A novel computer vision-based approach to automatic detection and severity assessment of crop diseases. https://doi.org/10.1109/sai.2015.7237209
Harakannanavar SS, Rudagi JM, Puranikmath VI, Siddiqua A, Pramodhini R (2022) Plant leaf disease detection using computer vision and machine learning algorithms. Glob Transit Proc. https://doi.org/10.1016/j.gltp.2022.03.016
Jabir B, Falih N (2022) Deep learning-based decision support system for weeds detection in wheat fields. Int J Electr Comput Eng 12(1):816. https://doi.org/10.11591/ijece.v12i1.pp816-825
Jones JW (1993) Decision support systems for agricultural development. In: Systems approaches for agricultural development: Proceedings of the International Symposium on Systems Approaches for Agricultural Development, 2–6 December 1991, Bangkok, Thailand. Springer, pp 459–471
Kamyab T, Delrish A, Daealhaq H, Ghahfarokhi AM, Beheshtinejad F (2022) Comparison and review of face recognition methods based on Gabor and boosting algorithms. Int J Robot Control Syst 2(4). http://pubs2.ascee.org/index.php/ijrcs
Lal H, Jones JW, Peart RM, Shoup WD (1992) FARMSYS—a whole-farm machinery management decision support system. Agric Syst 38(3):257–273. https://doi.org/10.1016/0308-521x(92)90069-z
Lefebvre M, Gil S, Brunet D, Natonek E, Baur C, Gugerli P, Pun T (1993) Computer vision and agricultural robotics for disease control: the potato operation. Comput Electron Agric 9(1):85–102. https://doi.org/10.1016/0168-1699(93)90031-u
Liu Y, Ma X, Shu L, Hancke GP, Abu-Mahfouz AM (2021) From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans Industr Inform 17(6):4322–4334. https://doi.org/10.1109/tii.2020.3003910
Mavridou E, Vrochidou E, Papakostas GA, Pachidis T, Kaburlasos VG (2019) Machine vision systems in precision agriculture for crop farming. J Imaging 5(12):89. https://doi.org/10.3390/jimaging5120089
Nanehkaran YA, Zhang D, Chen J, Tian Y, Al-Nabhan N (2020) Recognition of plant leaf diseases based on computer vision. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02505-x
Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Tona E, Hočevar M, Baur J, Pfaff J, Schütz C, Ulbrich H (2016) Selective spraying of grapevines for disease control using a modular agricultural robot. Biosyst Eng 146:203–215. https://doi.org/10.1016/j.biosystemseng.2015.12.004
Ouhami M, Hafiane A, Es-Saady Y, El Hajji M, Canals R (2021) Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sens 13(13):2486. https://doi.org/10.3390/rs13132486
Ramesh S, Hebbar R, Niveditha M, Pooja R, Shashank N, Vinod PV (2018) Plant disease detection using machine learning. In: 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C). IEEE, pp 41–45
Rastogi A, Arora R, Sharma S (2015) Leaf disease detection and grading using computer vision technology & fuzzy logic. In: 2015 2nd International conference on signal processing and integrated networks (SPIN). IEEE, pp 500–505
Rout R, Parida P (2020) A review on leaf disease detection using computer vision approach. In: Intelligent techniques and applications in science and technology: proceedings of the first international conference on innovations in modern science and technology 1. Springer, pp 863–871
Roy AM, Bhaduri J (2021) A deep learning enabled multi-class plant disease detection model based on computer vision. AI 2(3):413–428. https://doi.org/10.3390/ai2030026
Rupnik R, Kukar M, Vračar P, Košir D, Pevec D, Bosnić Z (2019) AgroDSS: a decision support system for agriculture and farming. Comput Electron Agric 161:260–271. https://doi.org/10.1016/j.compag.2018.04.001
Shitole S, Kale R (2021) Analysis of crop disease detection with SVM, KNN and random forest classification. Inf Technol Ind 9(1):364–372. https://doi.org/10.17762/itii.v9i1.140
Sowjanya KD, Sindhu R, Parijatham M, Srikanth K, Bhargav P (2017) Multipurpose autonomous agricultural robot. In: 2017 International conference of electronics, communication and aerospace technology (ICECA), vol 2. IEEE, pp 696–699
Tian H, Wang T, Liu Y, Qiao X, Li Y (2020) Computer vision technology in agricultural automation—a review. Inf Process Agric 7(1):1–19. https://doi.org/10.1016/j.inpa.2019.09.006
Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Eco Inform 63:101289. https://doi.org/10.1016/j.ecoinf.2021.101289
Waard MA, Georgopoulos SG, Hollomon DW, Ishii H, Leroux P, Ragsdale NN, Schwinn FJ (1993) Chemical control of plant diseases: problems and prospects. Annu Rev Phytopathol 31(1):403–421. https://doi.org/10.1146/annurev.py.31.090193.002155
Wang T, Chen B, Zhang Z, Li H, Zhang M (2022) Applications of machine vision in agricultural robot navigation: a review. Comput Electron Agric 198:107085. https://doi.org/10.1016/j.compag.2022.107085
Zhai Z, Martínez JF, Beltran V, Martínez NL (2020) Decision support systems for agriculture 4.0: survey and challenges. Comput Electron Agric 170:105256. https://doi.org/10.1016/j.compag.2020.105256
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Anand, R., Madhusudan, B.S., Bhalekar, D.G. (2024). Computer Vision and Agricultural Robotics for Disease Control. In: Chouhan, S.S., Singh, U.P., Jain, S. (eds) Applications of Computer Vision and Drone Technology in Agriculture 4.0. Springer, Singapore. https://doi.org/10.1007/978-981-99-8684-2_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-8684-2_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8683-5
Online ISBN: 978-981-99-8684-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)