Abstract
Plant ailments are the biggest issue, affecting productivity in agriculture, causing substantial losses in revenue and instability in nutritional supply. About 140 nations manufacture and normally cultivate citrus fruit harvests, which are quite significant for plants economically. Sadly, a variety of factors, including parasites and viruses, have a significant influence on citrus growing and have caused significant crop quality and yield reductions. A fast and precise diagnosis is crucial to halt the spreading of diseases of plants and reduce crop damage. The effectiveness of several optimizers including Adam, SGD, and RMSprop for identifying and categorizing citrus plant leaves and fruits illnesses is examined in this research along with a novel AI-based deep convolutional neural network (CNN) model. This model is trained with seven different classes of plant leaves and fruits images. The performance matrix provides a visual representation of optimizers’ effectiveness. We discovered that using data enhancement can enhance the model’s accuracy. To train the suggested model, various training epochs, batch sizes, and dropouts were employed. The recommended model outperforms the other one while using the Adam optimizer. The proposed model successfully classifies cropped images with a precision of 98.6%, proving that the algorithm may be utilized as an outline for categorizing images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khan MA et al (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 155:220–236. https://doi.org/10.1016/j.compag.2018.10.013
Maltesh IG et al (2020) Plant disease detection and its solution using image classification. Int J Futur Res Dev 01(01):73–79. https://doi.org/10.46625/ijfrd.2020.1109
Moriya ÉAS et al (2021) Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data. Comput Electron Agric 188. https://doi.org/10.1016/j.compag.2021.106298
Ngugi LC et al (2021) Recent advances in image processing techniques for automated leaf pest and disease recognition—a review. Inf Process Agric 8(1):27–51. https://doi.org/10.1016/j.inpa.2020.04.004
Nanehkaran YA et al (2020) Recognition of plant leaf diseases based on computer vision. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02505-x
Jagan K et al (2016) Detection and recognition of diseases from paddy plant leaf images. Int J Comput Appl 144(12):34–41. https://doi.org/10.5120/ijca2016910505
Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96–107
Liu Z et al (2021) Image recognition of citrus diseases based on deep learning. Comput Mater Contin 66(1):457–466. https://doi.org/10.32604/cmc.2020.012165
Liu J et al (2021) Plant diseases and pests detection based on deep learning: a review. Plant Methods 17(1). https://doi.org/10.1186/s13007-021-00722-9
Sharif M et al (2010) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234. https://doi.org/10.1016/j.compag.2018.04.023
Elaraby A et al (2022) Classification of citrus diseases using optimization deep learning approach. Comput Intell Neurosci 2022. https://doi.org/10.1155/2022/9153207
Ur Rehman MZ et al (2021) Classification of citrus plant diseases using deep transfer learning. Comput Mater Contin 70(1):1401–1417. https://doi.org/10.32604/cmc.2022.019046
Bhatnagar R et al (2021) AI based automatic detection of citrus fruit and leaves diseases using deep neural network model. J Discrete Math Sci Cryptogr 24(8):2181–2193. https://doi.org/10.1080/09720529.2021.2011095
Sharma G et al (2022) Cognitive framework and learning paradigms of plant leaf classification using artificial neural network and support vector machine. J Exp Theor Artif Intell. https://doi.org/10.1080/0952813X.2022.2096698
Bhatnagar R et al (2022) Automatic detection and recognition of citrus fruit and leaves diseases for precision agriculture. JUCS J Univ Comput Sci 28(9):930–948. https://doi.org/10.3897/jucs.94133
Bhatnagar R et al (2021) Citrus fruits diseases detection and classification using transfer learning. In: Proceedings of the international conference on data science, machine learning and artificial intelligence
Author information
Authors and Affiliations
Corresponding author
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 paper
Cite this paper
Saini, A.K., Bhatnagar, R., Srivastava, D.K. (2024). Citrus Fruits–Leaves Diseases Detection and Classification with Optimized Deep CNN. In: Nagar, A.K., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. WorldS4 2023. Lecture Notes in Networks and Systems, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-99-8031-4_9
Download citation
DOI: https://doi.org/10.1007/978-981-99-8031-4_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8030-7
Online ISBN: 978-981-99-8031-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)