Improved Deep Learning-Based Classifier for Detection and Classification of Aloe Barbadensis Miller Disease
Keywords:
Image Analysis, Leaf classification, convolutional neural networks (CNN), Transfer Learning, Data AugmentationAbstract
Aloe Barbadensis miller commonly known as Aloe Vera, is a widely grown medicinal plant with various health benefits. It is susceptible to several diseases, which can affect the quality and quantity of its yield. Farmers or experts examine the plants with their own eyes to find and recognize the disease. This process could be expensive, time-consuming, and inaccurate. Automatic detection generates quick and accurate findings by using image processing methods. This paper explores a strategy for classifying leaf images with deep convolutional networks to develop a plant disease identification model. The advancements in computer vision have the power to improve and broaden the scope of plant protection techniques and create new possibilities for computer vision applications in precision agriculture. Naturally, the innovative training methods and methodology make system implementation simple. The whole process of creating this disease detection model, from image collecting to database construction and evaluation by agricultural experts to deep learning framework and in-depth CNN training, is described in detail throughout the article. This article describes a method for identifying plant illnesses that makes use of a deep convolutional neural network that has been trained and fine-tuned using a database of plant leaves with a variety of disease-specific characteristics. The created model's innovation comes in its simplicity, which allows it to discriminate between ill and healthy leaves and their surroundings using deep CNN. Healthy leaves and backdrop photos are grouped with other classes. The model's advancement and innovation lay on its capacity to reliably diagnose plant illnesses while maintaining a simple procedure. The features have been extracted from plant images using AlexNet and CNN with transfer learning and data augmentation. This paper deals with a deep learning approach for detecting and predicting Aloe Vera plant diseases. The proposed approach has potential applications in the agricultural industry, especially in the early detection of plant diseases to minimize yield losses. Our proposed approach achieves improved accuracy in detecting and predicting Aloe Vera plant diseases.
Downloads
References
Muhammad Nazeer, et al. "Severity recognition of aloe vera diseases using AI in the tensor flow domain." CMC-Computers Materials & Continua 66.2 (2021): 2199-2216, DOI:10.32604/cmc.2020.012257
Bhagat, Monu, et al. "Plant leaf disease classification using grid search based SVM." 2nd International Conference on Data, Engineering, and Applications (IDEA). IEEE, 2020, DOI: 10.1109/IDEA49133.2020.9170725
Shruthi, U., V. Nagaveni, and B. K. Raghavendra. "A review on machine learning classification techniques for plant disease detection." 2019 5th International Conference on advanced computing & communication systems (ICACCS). IEEE, 2019, DOI: 10.1109/ICACCS.2019.8728415.
Froldi, Guglielmina, et al. "Antiglycation activity and HT-29 cellular uptake of Aloe-Emodin, Aloin, and Aloe arborescens leaf extracts." Molecules 24.11 (2019): 2128, DOI: 10.3390/molecules24112128
Babu, Spoorthy N., S. Govindarajan, and Ayesha Noor. "Aloe vera and its two bioactive constituents in the alleviation of diabetes–proteomic & mechanistic insights." Journal of Ethnopharmacology 280 (2021): 114445, DOI: 10.1016/j.jep.2021.114445
Gauri Deshpande, Pratiksha Shinde, Saee Patil, Shweta Pawar, Prajakta Dhamdhere."Aloe Vera Plant Diseases Detection" International Journal of Research in Engineering, Science, and Management Volume 5, Issue 6, June 2022 https://www.ijresm.com | ISSN (Online): 2581-5792
Sánchez, Marta, et al. "Pharmacological update properties of Aloe vera and its major active constituents." Molecules 25.6 (2020): 1324, DOI: 10.3390/molecules25061324
Uda, M. N. A., et al. "Antimicrobial activity of plant extracts from aloe vera, citrus hystrix, sabah snake grass, and zingiber officinale against pyricularia oryzae that causes rice blast disease in paddy plants." IOP Conference Series: Materials Science and Engineering. Vol. 318. No. 1. IOP Publishing, 2018, DOI: 10.1088/1757-899X/318/1/012009
Aziz, Sumair, et al. "Image pattern classification for plant disease identification using local tri-directional features." 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2019, DOI:10.1109/IEMCON.2019.8936236
R. Meena Prakash, G. P. Saraswathy, G. Ramalakshmi, K. H. Mangaleswari, and T. Kaviya, "Detection of leaf diseases and classification using digital image processing," Proc. 2017 Int. Conf. Innov. Information, Embed. Commun. Syst. ICIIECS 2017, vol. 2018-Janua, pp. 1-4, 2018, DOI: 10.1109/ICIICCS2017.8275915.
A. Van Arendonk, "The development of the share of agriculture in GDP and employment May 2015," no. May 2015.
Padol, P.B., Yadav, A.A., 2016. “SVM classifier based grape leaf disease detection”, in 2016 Conference on Advances in Signal Processing (CASP). IEEE, Pune, India, pp. 175–179 https://doi.org/10.1109/CASP.2016.7746160.
S. KC and W. Lutz, "The human core of the shared socioeconomic pathways: Population scenarios by age, sex, and level of education for all countries to 2100," Glob. Environ. Chang., vol. 42, pp. 181-192, 2017, DOI: 10.1016/j.gloenvcha.2014.06.004.
A.M Reshmi, P Prameeja," Leaf Disease Detection Using CNN", www.ijcrt.org © 2022 IJCRT | Volume 10, Issue 3 March 2022 | ISSN: 2320-2882.
Sneha Patel, Dr. U.K Jaliya, Pranay Patel, “A survey on plant leaf disease detection”. ISSN: 2455-3778: Volume: 06, Issue No:04, April 2020. “International journal for modern trends in science and Technology”, DOI: 10.46501/IJMTST
Yin H, Gu Y, et al. “Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests”, Agriculture 2020, 10, 439; doi:10.3390/agriculture10100439
Banumathi P, Sakthisree T, Vidhyapriya S, “Content Based Image Retrieval Techniques in Identifying Plant Diseases using Android System”, IJCAT - International Journal of Computing and Technology, Volume 3, Issue 11, November 2016, ISSN: 2348 – 6090.
B. Chandra, K. Viswavidyalaya, and W. Bengal, "Production Performance of Chilli in India-A Statistical Approach Production Performance of Chilli in India- A Statistical Approach," no. March 2017.
Yin H, Gu Y, et al. “Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears”, Agriculture 2022, 12, 300. DOI: https://doi.org/10.3390/agriculture12020300.
J. Tsopoe and N. Murry, "Economics of Chilli Cultivation in Wokha District of Nagaland, India," Curr. Agric. Res. J., vol. 8, no. 1, pp. 46-51, 2020, DOI: 10.12944/carj.8.1.08.
C. Penella and A. Calatayud, "Pepper Crop under Climate Change: Grafting as an Environmental Friendly Strategy," Clim. Resilient Agric. - Strateg. Perspect., no. March, 2018, DOI: 10.5772 intechopen.72361.
S. Lanka et al., "Chilli Outlook - February 2020," no. February, pp. 12-14, 2020.
Kaviani, Pouria, and Sunita Dhotre. "Short survey on naive Bayes algorithm." International Journal of Advance Engineering and Research Development 4, no. 11 (2017): 607-611.
Ameen, Fuad, Steven L. Stephenson, Saleh AlNadhari, and Mohamed A. Yassin. "Isolation, identification and bioactivity analysis of an endophytic fungus isolated from Aloe vera collected from Asir desert, Saudi Arabia." Bioprocess and biosystems engineering 44 (2021): 1063-1070.
Ahmmed, S. M., Md Maniruzzaman Sikder, Aireen Sultana, Sanjida Sultana, and Nuhu Alam. "First report on leaf spot disease of Aloe vera caused by Alternaria alternata (Fr.) Keissler in Bangladesh." International Journal of Botany Studies 5, no. 5 (2020): 164-169.
Kodors, S., G. Lacis, O. Sokolova, V. Zhukovs, I. Apeinans, and T. Bartulsons. "Apple scab detection using CNN and Transfer Learning." (2021), DOI: https://doi.org/10.15159/AR.21.045
Bhuiyan, Md Rafiuzzaman, Md Abdullahil-Oaphy, Rifa Shanzida Khanam, and Md Sanzidul Islam. "MediNET: A deep learning approach to recognize Bangladeshi ordinary medicinal plants using CNN." In Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020), pp. 371-380. Springer Singapore, 2021.
Tian, Liguo, Yu Sun, Meng Li, Yuesong Wang, Jinqi Liu, and Chuang Liu. "Research on Plant Growth State Classification Based on CNN-LSTM." In Journal of Physics: Conference Series, vol. 1993, no. 1, p. 012036. IOP Publishing, 2021.
Ong, Jia Heng, Pauline Ong, and Woon Kiow Lee. "Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network." Journal of Information and Communication Technology 21, no. 3 (2022): 383-410.
https://hscactus.org/resources/digest/plant-care/aloe-pests-and- diseases/#:~:text=meibomiae%2C%20this%20fungal%20infection%20causes,on%20haworthia%2 0and%20gasteria%20leaves. (Retrieved -23.03.2023).
S. K. A., A.. Raj, V. Sharma, and V. Kumar.“Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language Using CNN”." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10-14.
Vyas, S., Mukhija, M.K., S.K.A. (2023). An Efficient Approach for Plant Leaf Species Identification Based on SVM and SMO and Performance Improvement. In: Kulkarni, A.J., Mirjalili, S., Udgata, S.K. (eds) Intelligent Systems and Applications. Lecture Notes in Electrical Engineering, vol 959. Springer, Singapore. https://doi.org/10.1007/978-981-19-6581-4_1.
Vyas, S., Mukhija, M.K. and S.K.A., 2023, June. Design and analysis of new algorithm for plant leaf species identification using by SVM and SMO. In AIP Conference Proceedings (Vol. 2782, No. 1). AIP Publishing.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
