Abstract
Plant diseases are a persistent threat to global food security due to their ability to damage crops. They account for 20–40% of loss of global food trade every year. The exploding global food trade, coupled with climate change, has led to the sustainability of native plant pests in the new environment, worsening the condition. Additionally, new plant pests and diseases continue to threaten staple crops. This sheds light on the need for the implementation of novel techniques to diagnose plant diseases to tackle the global food crises. Implementation of artificial intelligence (AI)-based methods such as machine learning (ML), deep learning (DL), and artificial neural networks can aid in overcoming such challenges by conducting early diagnosis of plant pests and diseases. In recent years, many research investigations conducted on plant disease detection using AI have offered valuable insights for agriculturists, botanical researchers, practitioners, and industrial professionals. The applications DL and ML methods for plant disease detection are growing rapidly. This chapter will shed light on recent cutting-edge research in this field, including the latest advancements involving AI-based plant disease detection. It will also address the trials and limitations related to the usage of AI-based methods for plant disease diagnosis.
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
Similar content being viewed by others
References
Agrios GN (2005) Plant pathology, 5th edn. Academic Press
Das A, Pradhan B, Jha AK (2020) Integrating artificial intelligence with IoT in precision agriculture for sustainable crop production: a review. Comput Electron Agric 173:105370
Elad Y, Pertot I (2014) Climate change impacts on plant pathogens and plant diseases. J Crop Improv 28(1):99–139
Ge D, Li J (2020) A review on applications of deep learning in plant disease detection. Comput Electron Agric 177:105612
Guidi G, Salgado R (2018) Robotics and artificial intelligence in agriculture: current applications and future challenges. Agroecol Sustain Food Syst 42(7):702–722
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition
Hughes G, Salathé M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. ar**v preprint ar**v:1512.03477
Jones DG (2009) Plant pathogens and principles of plant pathology. In: Plant pathology. Wiley-Blackwell, pp. 1–17
Jung M, Song JS, Shin AY (2023) Construction of deep learning-based disease detection model in plants. Sci Rep 13:7331. https://doi.org/10.1038/s41598-023-34549-2
Kamilaris A, Kartakoullis A (2021) Applications of machine learning in precision agriculture: a review. Precis Agric 22(3):397–425
Kamilaris A, Prenafeta-Boldú FX (2018a) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–79
Kamilaris A, Prenafeta-Boldú FX (2018b) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90
Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX (2021) A review on the practice of plant disease detection using convolutional neural networks. Inform Process Agric 8(1):11–28
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Lucas JA (2008) Plant pathology and plant diseases. In: Encyclopedia of life sciences. John Wiley & Sons, pp. 1–8
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Molin J, Kvarnheden A (2010) Decision support system for forecasting primary infection periods of apple scab based on wireless sensor network. Comput Electron Agric 70(1):77–84
Picon A, Onelli E, Azzarello E, Giordano C, Masi E, Moscatiello R et al (2020) The role of data pre-processing in plant image analysis: a case study on image-based plant phenoty**. Front Plant Sci 11:1–17
Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72(1):1–13
Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3(3):430–439
Schumann GL, D’Arcy CJ (2010) Essential plant pathology, 2nd edn. American Phytopathological Society
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v preprint ar**v:1409.1556
Singh D, Shrivastava S (2018) An overview of convolutional neural networks: architectures, applications, and challenges. Ar**v Preprint ar**v:1710.09829
Tadesse T, Mwebaze E, Vossen G (2020) Deep learning techniques for plant disease detection and diagnosis. In: Deep learning and convolutional neural networks for medical image computing. Springer, pp. 315–339
Torres-Sánchez J, López-Granados F, De Castro AI (2018) Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis Agric 19(5):770–786
Windstam ST, Schmale DG (2018) The complexity of diagnosing plant diseases. In: Plant disease diagnosis. Springer, pp. 1–18
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1–19
Zhou B, Khosla A, Lapedriza À, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition
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
Tammina, M.R., Sumana, K., Singh, P.P., Lakshmi, T.R.V., Pande, S.D. (2024). Prediction of Plant Disease Using Artificial Intelligence. In: Khamparia, A., Pandey, B., Pandey, D.K., Gupta, D. (eds) Microbial Data Intelligence and Computational Techniques for Sustainable Computing. Microorganisms for Sustainability, vol 47. Springer, Singapore. https://doi.org/10.1007/978-981-99-9621-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-9621-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9620-9
Online ISBN: 978-981-99-9621-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)