Open source disease analysis system of cactus by artificial intelligence and image processing
Article No.: 11, Pages 1 - 7
Abstract
There is a growing interest in cactus cultivation because of numerous cacti uses from houseplants to food and medicinal applications. Various diseases impact the growth of cacti. To develop an automated model for the analysis of cactus disease and to be able to quickly treat and prevent damage to the cactus. The Faster R-CNN and YOLO algorithm technique were used to analyze cactus diseases automatically distributed into six groups: 1) anthracnose, 2) canker, 3) lack of care, 4) aphid, 5) rusts and 6) normal group. Based on the experimental results the YOLOv5 algorithm was found to be more effective at detecting and identifying cactus disease than the Faster R-CNN algorithm. Data training and testing with YOLOv5S model resulted in a precision of 89.7% and an accuracy (recall) of 98.5%, which is effective enough for further use in a number of applications in cactus cultivation. Overall the YOLOv5 algorithm had a test time per image of only 26 milliseconds. Therefore, the YOLOv5 algorithm was found to suitable for mobile applications and this model could be further developed into a program for analyzing cactus disease.
References
[1]
Debra Lee Baldwin. 2017. Why Cactus is Popular [WWW Document]. Retrieved November 8, 2020 from https://debraleebaldwin.com/succulent-plants/cactus-new-black/
[2]
Florian C. Stintzing and Reinhold Carle. 2005. Cactus stems (Opuntia spp.): A review on their chemistry, technology, and uses. Molecular nutrition & food research, 49(2), 175-194. https://doi.org/10.1002/mnfr.200400071
[3]
Rebah, F.B. and Siddeeg, S.M. 2017. Cactus an eco-friendly material for wastewater treatment: A review. Journal of Materials and Environmental Sciences, 8(5), 1770-1782.
[4]
Piga, A. 2004. Cactus pear: a fruit of nutraceutical and functional importance. Journal of the Professional Association for Cactus Development, 6, 9-22.
[5]
Shetty, A.A., Rana, M.K. and Preetham, S.P. 2012. Cactus: a medicinal food. Journal of food science and technology, 49(5), 530-536.
[6]
U. Osuna-Martínez, J. Reyes-Esparza, and L. Rodríguez-Fragoso. 2014. Cactus (Opuntia ficus-indica): A Review on its Antioxidants Properties and Potential Pharmacological Use in Chronic Diseases. Nat Prod Chem Res 2: 153. https://doi.org/10.4172/2329-6836.1000153
[7]
Zimmermann, H.G. and Granata, G. 2002. Insect pests and diseases. Cacti: biology and uses, 235-254.
[8]
Sujin Chantarasa-ard. 2019. Tropical anthracnose. Retrieved November 12, 2020 from https://bit.ly/3kRG4hM
[9]
Hyun ji Cho, Seong Won Hong, Hyun-ju Kim and Youn-Sig Kwa. 2016. Development of a multiplex PCR method to detect fungal pathogens for quarantine on exported cacti. The plant pathology journal, 32(1), 53.
[10]
Chnunphon Noothongkaew. 2016. Plant rusts. Retrieved November 12, 2020 from https://sites.google.com/site/chnunphon45523/rokh-thi-phb-laea-satru/rokh-ra-snim
[11]
Ren, Shaoqing, "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015.
[12]
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.
[13]
Carter, F. and Van Leeuwen, W.J. 2018. Mapping saguaro cacti using digital aerial imagery in Saguaro National Park. Journal of Applied Remote Sensing., 12(3), 036016.
[14]
Chidchanok Srichaiwong, Paisarn Trakulsuk and Suradej Boonlue. 2014. Decision Support Systems For Longan Leaf Disease Diagnosis with Decision Tree Technical., Veridian E-Journal Science and Technology Silpakorn University Vol. 1 No. 6, 1-14.
[15]
L. R. Priya, G. Ignisha Rajathi and R. Vedhapriyavadhana. 2019. Crop disease detection and monitoring system., International Journal of Recent Technology and Engineering (IJRTE), 3050 – 3053.
[16]
A. Koirala, K. B. Walsh, Z. Wang and C. McCarthy. 2019. Deep learning for real‑time fruit detection and orchard fruit load estimation: benchmarking of MangoYOLO., Precision Agriculture, 1107 – 1135.
[17]
Grassroot Engineer. 2020. What is YOLO ?? In object detection job. Retrieved December 15, 2020 from https://bit.ly/3qpRFGf
[18]
Redmon, J. and Farhadi, A. 2017. YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7263-7271.
[19]
Redmon, J. and Farhadi, A. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
[20]
Bochkovskiy, A., Wang, C.Y. and Liao, H.Y.M. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
[21]
Glenn Jocher. yolov5. Retrieved December 18, 2020 from https://github.com/ultralytics/yolov5
[22]
Use YOLO V5 to train an autonomous driving target detection network. Retrieved December 18, 2020 from https://www.programmersought .com/article/77866758708/
[23]
Leonardo Araujo dos Santos. 2020. Object Localization and Detection. Retrieved June 2, 2021 from https://leonardoaraujosantos.gitbooks.io/ artificial-inteligence/content/object_localization_and_detection.html
[24]
Joseph Nelson, Jacob Solawetz. 2020. Responding to the Controversy about YOLOv5 [WWW Document]. Roboflow Blog. Retrieved December 20, 2020 from https://blog.roboflow.com/yolov4-versus-yolov5/
[25]
YOLO5 license. Retrieved December 18, 2020 from https://github.com/ultralytics/yolov5/blob/master/LICENSE
[26]
Kittisak Phormraksa. 2020. Epic YOLO object detection. Retrieved December 20, 2020 from https://bit.ly/2O34h98
[27]
M. Bramer. 2007. Principles of Data Mining., Springer-Verlag London Limited: London.
[28]
Mr.P L. How Important is Evaluate Model?: Machine Learning 101. Retrieved December 20, 2020 from https://medium.com/mmp-li/evaluate-model-precision-recall-f1-score-machine-learning-101-89dbbada0c96
[29]
Goettsch, B., Hilton-Taylor, C., Cruz-Piñón, G., Duffy, J.P., Frances, A., Hernández, H.M., Inger, R., Pollock, C., Schipper, J., Superina, M. and Taylor, N.P. 2015. High proportion of cactus species threatened with extinction. Nature plants, 1(10), 1-7. https://doi.org/10.1038/nplants.2015.142
[30]
Oldfield, S. 1997. Cactus and succulent plants: status survey and conservation action plan. International Union for Conservation of Nature and Natural Resources (IUCN).
[31]
Reddy, J. 2019. Cactus Farming, Cultivation Practices of Cactus | Agri Farming [WWW Document]. Retrieved November 28, 2020 from https://www.agrifarming.in/cactus-farming-cultivation-practices-of-cactus
[32]
Vazquez-Mendoza, P., Miranda-Romero, L.A., Aranda-Osorio, G., Burgueño-Ferreira, J.A. and Salem, A.Z. 2017. Evaluation of eleven Mexican cultivars of prickly pear cactus trees for possibly utilization as animal fed: in vitro gas production. Agroforestry Systems., 91(4), 749-756.
[33]
CactusWay. 2020. Can Cactus Survive The Winter? (Tips For Protecting Your Cacti) [WWW Document]. Retrieved November 28, 2020 from https://cactusway.com/can-cactus-survive-the-winter-tips-for-protecting-your-cacti/
[34]
Denkenberger, D. and Pearce, J.M. 2014. Feeding everyone no matter what: Managing food security after global catastrophe. Academic Press.
[35]
Denkenberger, D.C. and Pearce, J.M. 2015. Feeding everyone: Solving the food crisis in event of global catastrophes that kill crops or obscure the sun. Futures, 72, 57-68.
[36]
Alvarado, K.A., Mill, A., Pearce, J.M., Vocaet, A. and Denkenberger, D. 2020. Scaling of greenhouse crop production in low sunlight scenarios. Science of The Total Environment, 707, 136012.
[37]
Full Guide on Cactus Diseases, Pests And Treatments. Retrieved November 18, 2020 from https://plantinterrarium.com/full-guide-on-cactus-diseases-pests-and-treatments/
[38]
Cacti Pests & Diseases. Retrieved November 2, 2020 from https://cactiguide.com/cactipests/
[39]
Scott Calhoun. 2012. The Gardeners Guide to Cactus The 100 Best Paddles, Barrels, Columns, and Globes.
[40]
John Pilbeam. 2018. Happy Cactus Cacti, Succulents, And More.
[41]
Edward F. Anderson. 2001. The Cactus Family.
[42]
Programmersought. The meaning of [email protected] and [email protected]:0.95,YOLO. Retrieved December 27, 2020 from https://www.programmersought .com/article/84866344358/
[43]
Plants Kiative. Retrieved November 18, 2020 from https://bit.ly/3rCCBFL
Index Terms
- Open source disease analysis system of cactus by artificial intelligence and image processing
Index terms have been assigned to the content through auto-classification.
Recommendations
Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN
Highlights- Designed a strawberry multiple diseases detection model based on multiscale feature fusion Faster R_CNN.
- Created a dataset of multiple disease types on different parts of strawberry leaves, flowers, and fruits.
- The mAP for multiple ...
AbstractDisease has a significant impact on strawberry quality and yield, and deep learning has become an important approach for the detection of crop disease. To address the problems of complex backgrounds and small disease spots in strawberry disease ...
Artificial Intelligence Chronic Disease Management System Based on Medical Resource Perception
Artificial Intelligence and SecurityAbstractChronic diseases have the characteristics of high morbidity, low awareness, and high disability and fatalities, which have a huge impact on human health. How to optimize chronic disease management through existing technologies is a worthy research ...
High Efficiency Disease Detection for Potato Leaf with Convolutional Neural Network
AbstractThe potato is the fourth largest food crop and is grown in many places. Potato crops are mainly infected with fungi so they suffer from early blight diseases and late blight diseases. Crop diseases must be detected and recognized because plant ...
Comments
Information & Contributors
Information
Published In
June 2021
281 pages
ISBN:9781450390125
DOI:10.1145/3468784
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IAIT2021
IAIT2021: The 12th International Conference on Advances in Information Technology
June 29 - July 1, 2021
Bangkok, Thailand
Acceptance Rates
Overall Acceptance Rate 20 of 47 submissions, 43%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 66Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format