RESEARCH ON RECOGNITION OF CROP DISEASE AND INSECT PESTS BASED ON DEEP LEARNING IN HARSH ENVIRONMENT
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Abstract
One of the most significant elements that pose a significant risk to agricultural productivity is the presence of agricultural diseases and insect pests. Finding and naming pests as soon as they appear is one of the most efficient ways to cut down on the financial damage they do. In this study, a convolutional neural network was utilized to automatically detect illnesses that might affect crops. This data set was obtained from the public data set that was provided for the AI Challenger Competition in 2018. It contains 27 photos of diseases affecting 10 different crops. Training is carried out with the help of the Inception-ResNet-v2 model in this article. The direct edge in the cross-layer and the multi-layer convolution in the residual network unit of the model. Following the completion of the combined convolution process, it is triggered by the connection into the ReLu function. The findings of the experiments indicate that this model has an overall recognition accuracy of 86.1%, which substantiates the claim that it is successful. Following the completion of this model's training, we developed and deployed the WeChat applet of agricultural disease and insect pest identification. After that, we began the real testing process. The findings demonstrate that the system is capable of correctly identifying crop illnesses and providing the appropriate recommendations.
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References
J. Schmidhuber, ‘‘Deep learning in neural networks: An overview,’’ Neural Netw., vol.
, pp. 85–117, Jan. 2015. 171692 VOLUME 8, Ai et al.: Research on Recognition Model
of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments
A.-R. Mohamed, G. E. Dahl, and G. Hinton, ‘‘Acoustic modeling using deep belief
networks,’’ IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 14–22, Jan. 2012.
Y. Bengio and O. Delalleau, ‘‘On the expressive power of deep architec- tures,’’ in Proc.
th Int. Conf. Discovery Sci. Berlin, Germany, 2011, no. 1, pp. 18–36.
L. Deng, O. Abdel-Hamid, and D. Yu, ‘‘A deep convolutional neural network using
heterogeneous pooling for trading acoustic invariance with phonetic confusion,’’ in Proc.
IEEE Int. Conf. Acoust., Speech Signal Process., May 2013, pp. 6669–6673.
S. Datir and S. Wagh, ‘‘Monitoring and detection of agricultural disease using wireless
sensor network,’’ Int. J. Comput. Appl., vol. 87, no. 4, pp. 1–5, Feb. 2014.
O. López, M. Rach, H. Migallon, M. Malumbres, A. Bonastre, and J. Serrano,
‘‘Monitoring pest insect traps by means of low-power image sensor technologies,’’ Sensors,
vol. 12, no. 11, pp. 15801–15819, Nov. 2012.
N. Srivastav, G. Chopra, P. Jain, and B. Khatter, ‘‘Pest Monitor and control system
using WSN with special referance to acoustic device,’’ in Proc. 27th ICEEE, Jan. 2013, pp.
–99.
G. Athanikar and M. P. Badar, ‘‘Potato leaf diseases detection and clas- sification
system,’’ Int. J. Comput. Sci. Mobile Comput., vol. 5, no. 2, pp. 76–88, 2016.
H. Wang, G. Li, Z. Ma, and X. Li, ‘‘Application of neural networks to image
recognition of plant diseases,’’ in Proc. Int. Conf. Syst. Informat. (ICSAI), May 2012, pp.
–2164.
D. Samanta, P. P. Chaudhury, and A. Ghosh, ‘‘Scab diseases detection of potato
using image processing,’’ Int. J. Comput. Trends Technol., vol. 3, no. 1, pp. 109–113, 2012.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, ‘‘ImageNet classifica- tion with
deep convolutional neural networks,’’ in Proc. Int. Conf. Neu- ral Inf. Process. Syst. Red
Hook, NY, USA: Curran Associates, 2012, pp. 1097–1105.
K. He, X. Zhang, S. Ren, and J. Sun, ‘‘Deep residual learning for image
recognition,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 770–778.
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, ‘‘A comparative study of finetuning deep learning models for plant disease identification,’’ Comput. Electron. Agricult.,
vol. 161, pp. 272–279, Jun. 2019.
S. P. Mohanty, D. P. Hughes, and M. Salathé, ‘‘Using deep learning for imagebased plant disease detection,’’ Frontiers Plant Sci., vol. 7, p. 1419, Sep. 2016.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, ‘‘Deep
neural networks based recognition of plant diseases by leaf image classification,’’ in Proc.
Comput. Intell. Neurosci., May 2016, Art. no. 328980