CNN-based Approach for Efficient Bell Pepper Leaf Disease Recognition
Keywords:
Bell-pepper, Plant disease, Convolutional Neural Networks (CNN), Bacterial spot DiseaseAbstract
Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%.)
Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%).
References
Bhagat M, Kumar D, Mahmood R, Pati B, Kumar M. Bell pepper leaf disease classification using CNN. In 2nd international conference on data, engineering and applications (IDEA). 2020 Feb 28; 1–5.
Bagde S, Patil S, Patil S, Patil P. Artificial neural network based plant leaf disease detection. Int J Comput Sci Mob Comput. 2015 Apr; 4(4): 900–5.
Islam MN, Kashem MA, Akter M, Rahman MJ. An approach to evaluate classifiers for automatic disease detection and classification of plant leaf. In International Conference on Electrical, Computer and Telecommunication Engineering, RUET, Rajshahi-6204, Bangladesh. 2012 Dec; 626–629.
Chouhan SS, Kaul A, Singh UP, Jain S. Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access. 2018 Feb 12; 6: 8852–63.
Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric. 2019 Jun 1; 161: 280–90.
Arsenovic M, Sladojevic S, Anderla A, Stefanovic D. FaceTime—Deep learning based face recognition attendance system. In 2017 IEEE 15th International symposium on intelligent systems and informatics (SISY). 2017 Sep 14; 000053–000058.
Abdollahi K, Condict L, Hung A, Kasapis S. Examination of β-lactoglobulin-ferulic acid complexation at elevated temperature using biochemical spectroscopy, proteomics and molecular dynamics. Food Hydrocoll. 2023 Jan 1; 134: 108053.
Shubita DA. The impact of ownership structure and investment decisions on capital structure: an empirical study from Jordan. Luton, United Kingdom: University of Bedfordshire Business School; 2023; 1–221.
Ramaswamy Reddy A, Prasad EV, Reddy LS. Comparative analysis of brain tumor detection using different segmentation techniques. Int J Comput Appl. 2013 Nov; 82(14): 14–28.
Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. Machine learning sensors for diagnosis of COVID-19 disease using routine blood values for internet of things application. Sensors. 2022 Oct 17; 22(20): 7886.
Helene Semb. (2023 Apr 25). Fall Flair: The Vibrant Beauty of American Wisteria Leaves. [Online]. ShunCy. Available from: https://shuncy.com/article/american-wisteria-leaves
