A Review of Iot-Based Technologies for Identification and Monitoring of Rice Crop Diseases
Article Sidebar
Main Article Content
Abstract: Paddy is one of the main crops throughout the world. It plays the role of important food crops in most parts of Asia (Chen et. al.). Paddy is prone to a lot of diseases so there is a need of proper monitoring system to monitor the paddy crop during its production. There are three stages of production of paddy i.e.Vegetation, reproductive and ripening. The field of IoT helped a lot in the development of monitoring system to detect the growth as well as diseases of rice crops. This article solely focused on the review or study of different articles which proposed different monitoring and detection system for monitoring of rice crop. To conduct the study different papers have been reviewed and found that there are a lot of devices present to detect the crop production but none of them are 100 percent accurate.
Downloads
References
C. Chen & H. Mcnairn (2006) A neural network integrated approach for rice crop monitoring, International Journal of Remote Sensing, 27:7, 1367-1393, DOI: 10.1080/01431160500421507
Oza, Sandip & Panigrahy, S. & Parihar, J.. (2008). Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop. International Journal of Applied Earth Observation and Geoinformation. 10. 296-304. 10.1016/j.jag.2007.12.002.
Ali, Terteil. (2018). Precision Agriculture Monitoring System using Internet of Things (IoT). International Journal for Research in Applied Science and Engineering Technology. 6. 2961-2970. 10.22214/ijraset.2018.4493.
Minh N. D. , Son Dong Hoang, Trinh Mai Van, (2019), a review of precision agriculture In rice production in Vietnam, International Workshop On Icts For Precision Agriculture, 6 - 8 august 2019 Mardi Headquarters, Selangor, Malaysia
Campos-Taberner, Manuel & García-Haro, Francisco & Camps-Valls, Gustau & Grau-Muedra, Gonçal & Nutini, Francesco & Crema, Alberto & Boschetti, Mirco. (2016). Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sensing of Environment. 187. 102 - 118. 10.1016/j.rse.2016.10.009.
H Lubis et al 2019, Monitoring System of Rice Plant Growth Using Microcontroller Sensor J. Phys.: Conf. Ser. 1235 012116
Lagos-Ortiz, Katty & Medina, Jose & Alarcón-Salvatierra, Abel & Morán, Manuel & Javier, Del Cioppo Morstadt & Valencia-García, Rafael. (2019). Decision Support System for the Control and Monitoring of Crops: Second International Conference, CITAMA 2019, Guayaquil, Ecuador, January 22-25, 2019, Proceedings. 10.1007/978-3-030-10728-4_3.
Rudiyanto, Rudiyanto & Minasny, Budiman & Shah, & Soh, & Arif, & Setiawan, Budi. (2019). Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform. Remote Sensing. 11. 1666. 10.3390/rs11141666.
Dirgahayu, Dede & Parsa, I. (2019). Detection Phase Growth of Paddy Crop Using SAR Sentinel-1 Data. IOP Conference Series: Earth and Environmental Science. 280. 012020. 10.1088/1755-1315/280/1/012020.
Sangeetha K., Santosh A. Prabeeda S.S. Selvamani T.(2019), Paddy Monitoring and Management System, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5 (2019) pp. 1045-1048
Niveditha, P. & Gururaj, H. & Janhavi, V.. (2019). An Analysis of Various Techniques for Leaf Disease Prediction: Proceedings of IEMIS 2018, Volume 3. 10.1007/978-981-13-1501-5_19.
Chen, Wen Liang & Lin, Yi-Bing & Ng, Fung Ling & Liu, Chun-You & Lin, Yun-Wei. (2019). RiceTalk: Rice Blast Detection using Internet of Things and Artificial Intelligence Technologies. IEEE Internet of Things Journal. PP. 1-1. 10.1109/JIOT.2019.2947624.
Zhang, Jingcheng & Pu, Ruiliang & González-Moreno, Pablo & Yuan, Lin & Wu, Kaihua & Huang, Wenjiang. (2019). Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture. 165. 104943. 10.1016/j.compag.2019.104943.
Nidhis, A. & Pardhu, Chandrapati & Reddy, K. & Kaliyaperumal, Deepa. (2019). Cluster Based Paddy Leaf Disease Detection, Classification and Diagnosis in Crop Health Monitoring Unit. 10.1007/978-3-030-04061-1_29.
Shrivastava, Vimal & Pradhan, Monoj & Minz, S. & Thakur, M.. (2019). RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLII-3/W6. 631-635. 10.5194/isprs-archives-XLII-3-W6-631-2019.
Gupta, Aman & Paudel, Jiwan & Yadav, Dipak & Kumar, Devansh & Yadav, Ramu & Kumar, Shrvan. (2019). Eco-friendly Management of False Smut (Ustilaginoidea virens) of Rice. International Journal of Current Microbiology and Applied Sciences. 8. 388-396. 10.20546/ijcmas.2019.811.049.
Devi TG, Srinivasan A, Sudha S, Narasimhan D. Web enabled paddy disease detection using Compressed Sensing. Math Biosci Eng. 2019 Aug 23;16(6):7719-7733. doi: 10.3934/mbe.2019387. PubMed PMID: 31698636.
Bhattacharya, Shreyasi & Mukherjee, Anirban & Phadikar, Santanu. (2020). A Deep Learning Approach for the Classification of Rice Leaf Diseases. 10.1007/978-981-15-2021-1_8.
Ennouri, Karim & Triki, Mohamed & Kallel, Abdelaziz. (2020). Applications of Remote Sensing in Pest Monitoring and Crop Management. 10.1007/978-981-13-9431-7_5.
Das, Ankur & Dutta, Ratnaprava & Das, Sunanda & Sengupta, Shampa. (2020). Feature Selection Using Graph-Based Clustering for Rice Disease Prediction. 10.1007/978-981-13-9042-5_50.
Kaur, M., & Mohana, R. (2018). A Software Agent Based Technique for Load Balancing in Partitioned Cloud. International Journal of Engineering & Technology, 7(4.12), 13-19.
Saini, G. S., Sharma, C., & Singh, G. (2017). Enhanced VHD Making Algorithm for 4G Heterogeneous Networks (Only Abstract).
Thakur, H., & Singh, G. (2016). Privacy and Ownership Protection Digital Data Techniques: Comparison and Survey. Indian Journal of Science and Technology, 9(47).
Sandhu, A. S., & Kalra, G. S. Survey of Microscopic Image Segmentation Techniques and Image Quality Measures.
Singh, M., Kaur, N., Kaur, A., & Pushkarna, G. (2017). A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks. International Journal of Cyber Warfare and Terrorism (IJCWT), 7(2), 42-53.
Kaur, N., Kaur, A., Singh, M., & Pushkarna, G. (2017). Detection of black hole node in wireless sensor networks using DSR protocol
Kaur, A., Kaur, N., Singh, M., & Pushkarna, G. (2016). Sensor Integrated RFID Basedpostal Letter Tracking System

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.