Image based Plant leaf disease detection using Deep learning

  • Poornam S Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India.
  • Francis Saviour Devaraj A Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India.
Keywords: Plant disease, Deep learning, CNN, Classification, Artificial Intelligence

Abstract

Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops.  There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs.

Metrics

Metrics Loading ...

References

Khattab, A., Habib, S. E., Ismail, H., Zayan, S., Fahmy, Y., & Khairy, M. M. (2019). An IoT-based cognitive monitoring system for early plant disease forecast. Computers and Electronics in Agriculture, 166, 105028. https://doi.org/10.1016/j.compag.2019.105028

Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017). Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and electronics in agriculture, 138, 200-209. https://doi.org/10.1016/j.compag.2017.04.013

Kamal, K. C., Yin, Z., Wu, M., & Wu, Z. (2019). Depthwise separable convolution architectures for plant disease classification. Computers and Electronics in Agriculture, 165, 104948. https://doi.org/10.1016/j.compag.2019.104948

Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience.

Tewari, V. K., Pareek, C. M., Lal, G., Dhruw, L. K., & Singh, N. (2020). Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop. Artificial Intelligence in Agriculture, 4, 21-30. https://doi.org/10.1016/j.aiia.2020.01.002

Varalakshmi, P., & Aravindkumar, S. (2019). Plant disorder precognition by image based pattern recognition. Procedia Computer Science, 165, 502-510.

Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H., ur Rehman, M. H., & Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and electronics in agriculture, 153, 12-32.

Parraga-Alava, J., Cusme, K., Loor, A., & Santander, E. (2019). RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition. Data in brief, 25, 104414. https://dx.doi.org/10.1016%2Fj.dib.2019.104414

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009

Shrivastava, V. K., Pradhan, M. K., Minz, S., & Thakur, M. P. (2019). Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 18–20. http://dx.doi.org/10.1007/s42161-020-00683-3

Picon, A., Seitz, M., Alvarez-Gila, A., Mohnke, P., Ortiz-Barredo, A., & Echazarra, J. (2019). Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Computers and Electronics in Agriculture, 167, 105093. https://doi.org/10.1016/j.compag.2019.105093

Nazki, H., Yoon, S., Fuentes, A., & Park, D. S. (2020). Unsupervised image translation using adversarial networks for improved plant disease recognition. Computers and Electronics in Agriculture, 168, 105117. https://doi.org/10.1016/j.compag.2019.105117

Barbedo, J. G. (2018). Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 172, 84-91.

Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2018). A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5(3), 354-371. https://doi.org/10.1016/j.inpa.2018.05.002

Shaikh, D. A., Ghorale Akshay, G., Chaudhari Prashant, A., Kale Parmeshwar, L., (2016). Intelligent Autonomous Farming Robot with Plant Disease Detection using Image Processing. International Journal of Advanced Research in Computer and Communication Engineering, 5. https://doi.org/10.17148/IJARCCE.2016.54248

Vijay Kumar, V., Vani, K.S., Acharya, (2018). Agricultural Robot: Leaf Disease Detection and Monitoring the Field Condition Using Machine Learning and Image Processing, International Journal of Computational Intelligence Research, 14 (7) 551-561.

P. Sharma, Y. Paul Singh Berwal, W. Ghai, Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation, Information Processing in Agriculture (2019).

Shirahatti, J., Patil, R., & Akulwar, P. (2018). A survey paper on plant disease identification using machine learning approach. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES), IEEE. 1171-1174.

Mokhtar, U., Ali, M. A., Hassanien, A. E., & Hefny, H. (2015). Identifying two of tomatoes leaf viruses using support vector machine. In Information Systems Design and Intelligent Applications, Springer, New Delhi. 771-782.

Steinwart, I., Christmann, A., (2008). Support Vector Machines, Springer Science & Business Media, New York, NY, USA.

Published
2021-05-30
How to Cite
S, P., & A, F. S. D. (2021). Image based Plant leaf disease detection using Deep learning. International Journal of Computer Communication and Informatics, 3(1), 53-65. https://doi.org/10.34256/ijcci2115



Views: Abstract : 112 | PDF : 42

Plum Analytics