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Real Time Leaf Disease Detection Using Deep Learning Method
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Due to regular occurrences of hot and humid climate of the country, crops are destroyed by invasion of certain diseases. As a result, entire farm gets affected and huge loss and damage happens for the farmers. This paper focuses on developing a system which detects at the onset of any disease by continuous monitoring of leaves. In addition, a moisture measuring device is also fitted which allows the microcontroller to spray water from a tank whenever there is a shortage. Secondly, leaf disease detection system achieved by deep learning, also instruct a second microcontroller to spray desired amount of pesticide as and where required. A web application made for this also instructs farmers what should be their next procedure whenever a certain disease is detected.The accuracy ofmodel is 94% when trained and tested on leaf dataset.
Keywords
CNN, Arduino IDE, Moister Sensor, OpenCV, Leaf Disease.
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- C H Chavan and P V Karande, Wireless Monitoring of Soil Moisture, Temperature & Humidity Using Zigbee in Agriculture, International Journal of Engineering Trends and Technology, Vol 11, No 10, page 493-497, 2014.
- Labidi, A Chouchaine and A K Mami, Control of Relative Humidity Inside an Agricultural Greenhouse, 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), page 109-114, 2017. doi: 10.1109/STA.2017.8314955
- B N Getu and H A Attia, Automatic Control of Agricultural Pumps Based on Soil Moisture Sensing, AFRICON-2015, page 1-5, 2015. doi: 10.1109/AFRCON.2015.7332052
- W Zhuang, J Zhi and L G Hong, Temperature and Humidity Measure-Control System Based on CAN and Digital Sensors, International Forum on Information Technology and Applications, page 548-550, 2009. doi: 10.1109/IFITA.2009.126
- R Romero, J L Muriel, I García and D M de la Peña, Research on Automatic Irrigation Control: State of the Art and Recent Results, Agricultural Water Management, Vol 114, page 59-66, 2012.
- J Rhee, I Jungho and C Gregory, Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment. Vol 114, page 2875-2887, 2010. https://doi.org/10.1016/j.rse.2010.07.005
- E Olakunle, A Rahman, T Orikumhi, I Leow, C Yen and H Mohammad, An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges, IEEE Internet of Things Journal, page 1, 2018. https://doi.org/10.1109/JIOT.2018.2844296
- W Ning, N Zhang and M Wang, Wireless Sensors in Agriculture and Food Industry—Recent Development and Future Perspective, Computers and Electronics in Agriculture, Vol 50, Page 1-14, 2006. https://doi.org/10.1016/j.compag.2005.09.003
- M Mekala and P Viswanathan, A Novel Technology for Smart Agriculture Based on IoT with Cloud Computing, page 75-82, 2017. https://doi.org/10.1109/I-SMAC.2017.8058280
- R Mundada and V Gohokar, Detection and Classification of Pests in Greenhouse Using Image Processing, IOSR J. Electr. Commun. Engg, Vol 5, page 57-63, 2013. https://doi.org/10.9790/2834-565763
- G Bhadane, S Sharma and V B Nerkar, Early Pest Identification in Agricultural Crops Using Image Processing Techniques, International Journal of Electrical, Electronics and Computer Engineering, Vol 2, No 2, page 77-82, 2013.
- A Saeed, N Adnan and S A Basit, Pest Detection and Control Techniques Using Wireless Sensor Network: A Review, Journal of Entomology and Zoology Studies, Vol 3, page 92-99, 2015.
- J Peng, X Hongbo, H Zhiye and W Zheming, Design of a Water Environment Monitoring System Based on Wireless Sensor Networks, Sensors, Vol 9, No 8, page 6411-6434, 2009. https://doi.org/10.3390/s90806411
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