An Agri vigilance System based on Computer Vision and Deep Learning
DOI:
https://doi.org/10.54060/JIEEE/002.02.017Keywords:
Precise farming, crop disease detection, livestock monitoring, ResNet, YOLOv3-tiny, Smart AgricultureAbstract
In an agricultural-based country like India, farming and farming activities play a vital role in the growth of the economy as it is the main source of GNI (Gross National In-come). This dependence of GNI on agriculture makes it important to address the issues faced by the farmers. The main area of concern for farmers revolves around crops and livestock. Precise farming techniques like cattle counting and crop disease detection are the need of the hour. The introduction of computer vision and deep learning has ena-bled us to make improvements in farming techniques. To accomplish this, a computer vision-based system is proposed which will be implemented using ResNet and YOLOv3-tiny. The proposed system will take images and videos as input and run them on the inference. The output will be updated in the database and the farmer will be noti-fied in case of any inconsistency. The detailed report can be accessed by government agencies. The system will increase efficiency in farming processes like crop monitoring, livestock tracking, crop disease detection by providing fast and efficient solutions for the problems faced by the farmers.
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J. Trivedi, Y. Shamnani, and R. Gajjar, “Plant leaf disease detection using machine learning,” in Communications in Computer and Information Science, Singapore: Springer Singapore, pp. 267–276,2020.
V. Kumar, H. Arora, Harsh, et al., “ResNet-based approach for detection and classification of plant leaf diseases,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 495-502, 2020.
H. Durmus, E. O. Gunes, and M. Kirci, “Disease detection on the leaves of the tomato plants by using deep learning,” in 6th International Conference on Agro-Geoinformatics, 2017.
H. Durmuş, E. O. Güneş and M. Kırcı, "Data acquisition from greenhouses by using autonomous mobile robot," Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1-5, 2016.
K. He, X. Zhang, S. Ren, et al., “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
J. G. A. Barbedo, L. V. Koenigkan, P. M. Santoset al., “Counting cattle in UAV images—dealing with clustered animals and animal/background contrast changes,” Sensors (Basel), vol. 20, no. 7, p. 2126, 2020.
W. Shao, R. Kawakami, R. Yoshihashi, et al., “Cattle detection and counting in UAV images based on convolutional neural networks,” Int. J. Remote Sens., vol. 41, no. 1, pp. 31–52, 2020.
J. C.V Gemert, C. R. Verschoor, P. Mettes, et al., “Nature conservation drones for automatic localization and counting of animals,” in Computer Vision – ECCV Workshops, Cham: Springer International Publishing, pp. 255–270,2015.
J. G. A. Barbedo, L. V. Koenigkan, T. T. Santos, et al., “A study on the detection of cattle in UAV images using deep learning,” Sensors (Basel), vol. 19, no. 24, 2019.
B. Xu, W. Wang, G. Falzon, et al., “Automated cattle counting using Mask R-CNN in quadcopter vision system,” Comput. Electron. Agric., vol. 171, no. 105300, 2020.
P. Singhal, P. Singh, and A. Vidyarthi, “Interpretation and localization of Thorax diseases using DCNN in Chest X-Ray, “Journal of Informatics Electrical and Electronics Engineering, vol.1, no.1, pp.1-7,2020.
M. Vinny, & P. Singh, “Review on the Artificial Brain Technology: Blue Brain, “Journal of Informatics Electrical and Electronics Engineering, vol.1, no.1, pp.1-11,2020.
A. Singh and P. Singh, “Object Detection,” Journal of Management and Service Science, “vol.1, no.2, pp. 1-20,2021
A. Singh, & P. Singh, “Image Classification: A Survey, “Journal of Informatics Electrical and Electronics Engineering, vol.1, no.2, pp.1-9,2020.
A. Singh and P. Singh, “License Plate Recognition, “Journal of Management and Service Science, vol.1, no.2, pp. 1-14,2021.
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