Early Wheat Leaf Disease Detection Using CNN

Abstract:

Smart farming is an innovative technology that aids in the improvement of the country's agricultural produce quality and quantity. Wheat is the most important crop in most parts of India. Wheat leaf diseases have a significant impact on production rates and farmer earnings. It poses a significant danger to food security because it affects crop productivity and degrades crop quality. Accurate and precise disease detection has posed a significant challenge, but recent advances in computer vision enabled by deep learning have paved the road for camera-assisted wheat leaf disease diagnosis. Using a CNN trained with a publicly available wheat leaf disease model, several machine learning algorithms and neuron- and layer-wise visualization methods are applied.

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 295-301
• DOI: https://doi.org/10.4028/p-653bh6
• Online since: February 2023
• Authors: R Vedika, M.Mithra Lakshmi*, R. Sakthia, K. Meenakshi
• Keywords: Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), SVM (Support Vector Machine) Model

References

[1] J. Su et al., Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring,, in IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp.2242-2249, March-(2021).

DOI: 10.1109/tii.2020.2979237

[2] Sumit Nema, Bharat Mishra and Mamta Lambert ,Android Application of Wheat Leaf Disease Detection and Prevention using Machine Learning,, International Journal of Trend in Research and Development, Volume 7(2), Apr-(2020).

[3] Er.Varinderjit Kaur, Dr.Ashish Oberoi, Wheat diesease detection using SVM classifier,, JETIR August 2018, Volume 5, Issue 8, August-(2018).

[4] Amina Khatra,Yellow Rust Extraction in Wheat Crop based on Color Segmentation Techniques,, IOSR Journal of Engineering, Vol. 3, Issue 12, December-(2013).

DOI: 10.9790/3021-031215658

[5] Ashwini T Sapkal, Uday V Kulkarni, Comparative study of Leaf Disease Diagnosis system using Texture features and Deep Learning Features, , International Journal of Applied Engineering Research, Volume 13, Number 19 , (2018).

[6] Simranjeet Kaur, Geetanjali Babbar, Navneet Sandhu, Dr. Gagan Jindal, Various plant disease detection using image processing methods,, IJSDR ,Volume 4, Issue 6, June-(2019).

[7] Mrinal Kumar, Tathagata Hazra , Dr. Sanjaya Shankar Tripathy, Wheat Leaf Disease Detection Using Image Processing,, IJLTEMAS, Volume VI, Issue IV, April (2017).

[8] Aarju Dixit , Sumit Nema,Wheat Leaf Disease Detection Using Machine Learning Method- A Review,,IJCSMC, Vol. 7, Issue.5, May (2018).

[9] M. S. Hossain, M. Al-Hammadi, and G. Muhammad, Automatic fruit classification using deep learning for industrial applications,, IEEE Trans. Ind. Informat., vol. 15, no. 2, Feb. (2019).

DOI: 10.1109/tii.2018.2875149

[10] Bashish Dheeb Al Bashish, Malik Braik and Sulieman Bani-Ahmad, 2011. Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural networks based Classification,. Information Technology Journal, 10: 267-275.

DOI: 10.3923/itj.2011.267.275

[11] E. Hamuda, M. Glavin, and E. Jones, A survey of image processing techniques for plant extraction and segmentation in the field,, Comput. Electron. Agriculture, vol. 125, p.184–199, (2016).

DOI: 10.1016/j.compag.2016.04.024

[12] P. Kumsawat, A. Menukaewjinda, K. Attakitmongcol and ASrikaew,Grape leaf disease detection from color imagery using hybrid intelligent system,, ECTI-CON (2008).

DOI: 10.1109/ecticon.2008.4600483

[13] Dr.K.Thangadurai and K.Padmavathi, Computer Vision image Enhancement for Plant Leaves Disease Detection,, WCCCT (2014).

[14] Monica Jhuria, Rushikesh Borse and Ashwani Kumar,Image Processing For Smart Farming: Detection Of Disease And Fruit Grading,, IEEE (2013).

DOI: 10.1109/iciip.2013.6707647

[15] Zhang Jian and Zhang Wei,Support vector machine for recognition of cucumber leaf diseases,, IEEE (2010).

DOI: 10.1109/icacc.2010.5487242

[16] A. Srobárová, L. Kakalíková, Fungal disease of grapevines,,. Eur. J. Sci. Biotechnol, vol. 1, p.84–90, 2007.fu.

[17] S. Zhang, X. Wu, Z. You, L. Zhang, Leaf image based cucumber disease recognition using sparse representation classification," Comput. Electron. Agric. vol. 134, pp.135-141, (2017).

DOI: 10.1016/j.compag.2017.01.014

[18] S. Fiel, R. Sablatnig, Automated identification of tree species from images of the bark,, leaves and needles. In: Proc. of 16th Computer Vision Winter Workshop, Mitterberg, Austria, p.1–6, (2011).

[19] Ashwini Awate, Gayatri Amrutkar, Damini Deshmankar and Utkarsh Bagul, Fruit disease detection using color, texture analysis and ANN,, IGCIoT (2015).

DOI: 10.1109/icgciot.2015.7380603

[20] Zulkifli Bin Husin, Ali Yeon Bin MdShakaff, Abdul Hallis Bin Abdul Aziz and Mohamed Farook,Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques,, ISMS (2012).

DOI: 10.1109/isms.2012.33