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Sudheer Chakravarthi, P.
- Real Time corn Leaf Disease Detection Using convolution Neural Network
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Authors
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1 Department of Electronics and Communication Engineering, Sri Vasavi Engineering College, IN
1 Department of Electronics and Communication Engineering, Sri Vasavi Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2837-2843Abstract
Agriculture is the primary resource of livelihood, and the economy of our country highly depends on agricultural productivity. For this reason, plant disease detection places a vital role in the agriculture sector. According to one survey, in India, nearly 70% of the population depends on agriculture which is composed of many crops. Disease identification in plants is very challenging for farmers as well as for researchers. We proposed a 24-layer deep learning model in our paper using convolution neural networks (CNN) for the detection of corn leaf diseases by using real time image dataset as input. The CNN model is trained with different corn leaf image samples and model performance is tested and is reported with the evaluation metrics. The obtained results are compared with CNN pre-defined models which shows the superior performance of the proposed model compared to other state-of-the-art approaches.Keywords
Agriculture, Plant Disease Detection, Deep Learning, Convolution Neural Networks, Corn Leaf Image.
References
- Sneha Adhikari, “Identification of QTL for Banded Leaf and Sheath Blight in Teosinte-Derived Maize Population”, Agricultural Research, Vol. 65, pp. 1-9, 2021.
- Xanthoula Eirini, Dimitrios Moshou and Alexandra A. Tamouridou. “Automated Leaf Disease Detection in Different Crop Species through Image Features Analysis and One Class Classifiers”, Computers and Electronics in Agriculture, Vol. 156, pp. 96-104, 2019.
- P. Ferentinos, Konstantinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018.
- S.P. Mohanty, David P. Hughes and Marcel Salathe, “Using Deep Learning for Image-Based Plant Disease Detection”, Frontiers in Plant Science, Vol. 7, pp. 1419-1428, 2016.
- Reva Nagi and Sanjaya Shankar Tripathy, “Severity Estimation of Grapevine Diseases from Leaf Images Using Fuzzy Inference System”, Agricultural Research, Vol. 11, No. 1, pp. 112-122, 2022.
- D.R. Smith and D. G. White, “Diseases of Corn”, Corn and Corn Improvement, Vol. 18, pp. 687-766, 1988.
- D.A. Noola and R.B. Dayanand, “Computer Aided Corn Leaf Disease Identification System”, Proceedings of International Conference on Smart Electronics and Communication, pp. 1-13, 2021.
- Firouzabadi, “Performance Evaluation of Supervised Classification of Remotely Sensed Data for Crop Acreage Estimation”, Proceedings of International Conference on International Geoscience and Remote Sensing, pp. 1-13, 2001.
- Haiguang Wang, “Application of Neural Networks to Image Recognition of Plant Diseases”, Proceedings of International Conference on Systems and Informatics, pp. 1-13, 2012.
- Haiguang Wang, “Image Recognition of Plant Diseases based on Principal Component Analysis and Neural Networks”, Proceedings of International Conference on Natural Computation, pp. 1-14, 2012.
- S. Varshney and Tarun Dalal, “Plant Disease Prediction using Image Processing Techniques-A Review”, International Journal of Computer Science and Mobile Computing, Vol. 5, No. 5, pp. 394-398, 2016.
- C. De Chant, “Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery using Deep Learning”, Phytopathology, Vol. 107, No. 11, pp. 1426-1432, 2017.
- P. Ferentinos, Konstantinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018.
- Chao Ni, “Automatic Inspection Machine for Maize Kernels based on Deep Convolutional Neural Networks”, Biosystems Engineering, Vol. 178, pp. 131-144, 2019.
- S. Mishra, Rishabh Sachan and Diksha Rajpal, “Deep Convolutional Neural Network based Detection System for Real-Time Corn Plant Disease Recognition”, Procedia Computer Science, Vol. 167, pp. 2003-2010, 2020.
- H. Yu, “Corn Leaf Diseases Diagnosis based on K-Means Clustering and Deep Learning”, IEEE Access, Vol. 9, pp. 143824-143835, 2021.
- T. Muthusamy and Gomathi Eswaran, “Detection of Sugarcane Mosaic Diseases using Deep Learning Architecture to Avoid Annealing Temperature of PCR Primer in Laboratory Testing”, Traitement Du Signal, Vol. 39, No. 1, pp. 1-13, 2022.
- Ian Goodfellow, YoshuaBengio and Aaron Courville, “Deep Learning”, MIT press, 2016.
- Saad Albawi, Tareq Abed Mohammed and Saad Al-Zawi, “Understanding of a Convolutional Neural Network”, Proceedings of International Conference on Engineering and Technology, pp. 1-8, 2017.
- S. Ioffe and Christian Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, Proceedings of International Conference on Machine Learning, pp. 1-13, 2015.
- Hidenori Ide and Takio Kurita, “Improvement of Learning for CNN with ReLU Activation by Sparse Regularization”, Proceedings of International Conference on Neural Networks, 2017.
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet Classification with Deep Convolutional Neural Networks”, Proceedings of International Conference on Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
- Karen Simonyan and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of International Conference on Machine Learning, pp. 1-9, 2014.
- C. Szegedy, “Going Deeper with Convolutions”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2015.
- K. He, “Deep Residual Learning for Image Recognition”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 891-898, 2016.