International Journal of Analog Integrated Circuits
Iot Based Smart Farming with Plant Disease Detection Using CNN
Abstract
In this project, we have discussed the utilization of the power of contemporary technology like sensors and cloudcomputing. IoT-based smart farming is revolutionizing traditional agricultural practices. Additionally, this project enables farmersto manage their farms remotely. This project's primary goal is to support an existing farmer and a new farmer who wants to startfarming as their side income source through remote farming. Farmers can monitor numerous environmental elements includingtemperature, humidity, soil moisture levels, and tank water levels in real time with the use of IoT devices. They can also controlthe fertilization process and pests’ control remotely. They can monitor their crops using a robot, they can get live video footage ontheir device. Users can take photographs from the video footage provided by the robot and can upload those photos on the webpage to get information about the crop’s diseases. It will help the farmer maximize agricultural yield, lower waste, and boostprofitability. Additionally, smart farming systems give farmers useful knowledge about crop development and disease prevention,empowering them to make data-driven decisions and take preventative action to avoid crop losses. IoT-based smart farmingsystems may also be remotely accessed and managed via smartphones and other connected devices, giving farmers the flexibilityto manage their operations whenever and wherever they choose.References (17)
- G. Sushanth and S. Sujatha, "IOT Based Smart Agriculture System," 2018 International Conference on Wireless
- Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2018, pp. 1-4, Doi:
- 1109/WiSPNET.2018.8538702.
- Jash Doshi, Tirth Kumar Patel, Santosh Kumar Bharti, Smart Farming using IoT, a solution for optimal monitoring, The 3rd
- International Workshop on Recent advances on Internet of Things: Technology and application Approaches (IoT-T&A 2019)
- November 4-7, 2019, Coimbra, Portugal, page 6, j.procs.2019.11.016
- . Krishnamurthy B, Divya M, Abhishek S, Shashank H, Solar Fencing Unit and Alarm for Animal Entry Prevention,
- International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137, Volume – 02, Issue – 05, May
- – 2017, PP – 128-135
- M. Shobana et al., "Plant Disease Detection Using Convolution Neural Network,", 2022 International Conference on
- Computer Communication and Informatics (ICCCI), Coimbatore, India, 2022, pp. 1-5, doi:10.1109/ICCCI54379.2022.9740975.
- L. Ale, A. Sheta, L. Li, Y. Wang and N. Zhang, "Deep Learning Based Plant Disease Detection for Smart Agriculture,", to
- evaluate the performance of the proposed models based on real- world datasets, which demonstrate the proposed models can
- accurately detect plant disease using low computational resources, 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI,
- USA, 2019, pp. 1-6, doi:10.1109/GCWkshps45667.2019.9024439.
- Punam Bedi, Pushkar Gole, Plant disease detection using hybrid model based on convolutional autoencoder and convolutional
- neural network. Artificial Intelligence in Agriculture, Volume 5, 2021, Pages 90-101