Deep Learning Based Tomato PLDD

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Bade Ashwini Vivekanand, M. Suresh Kumar
DOI : 10.14445/22315381/IJETT-V70I7P243

How to Cite?

Bade Ashwini Vivekanand, M. Suresh Kumar, "Deep Learning Based Tomato PLDD" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 414-421, 2022. Crossref,

Agriculture sector is the prime source of food and industrial raw material that satisfies the increasing population demand and industrial revolution. However, plant leaf disease detection (PLDD) degrades the quality of food and agricultural products, leading to economic loss for farmers. Recently, many deep learning frameworks have been presented for the PLDD that has shown gigantic improvement over traditional machine learning-based leaf disease detection. The performance of these deep learning frameworks is often limited due to lower feature variability, data scarcity problem, and low accuracy for multiple plant disease detection. This article presents PLDD based on a deep convolutional neural network (DCNN) to improve the feature variability and disease detection accuracy. The effectiveness of the proposed approach is evaluated on tomato plants from the PlantVillage dataset. The proposed method provides 98.83% and 96.06% accuracy in 2-class and 9-class for PLDD.

Agricultural Automation, PLDD, Deep Learning, Precision Agriculture, Convolutional Neural Network.

[1] Li, Lili, Shujuan Zhang, and Bin Wang, “Plant Disease Detection and Classification By Deep Learning—A Review,” IEEE Access, Vol.9, Pp.56683-56698, 2021.
[2] Sujatha, R., Jyotir Moy Chatterjee, N. Z. Jhanjhi, and Sarfraz Nawaz Brohi, “Performance of Deep Learning Vs. Machine Learning In PLDD, Microprocessors, and Microsystems,” Vol.80 , Pp.103615, 2021.
[3] Jogekar, Ravindranamdeorao, and Nandita Tiwari, “A Review of Deep Learning Techniques for Identification and Diagnosis of Plant Leaf Disease, Smart Trends In Computing and Communications,” Proceedings of Smartcom, Pp.435-441, 2020-2021.
[4] Vishnoi, Vibhor Kumar, Krishan Kumar, and Brajesh Kumar, “Plant Disease Detection Using Computational Intelligence and Image Processing,” Journal of Plant Diseases and Protection, Vol. 128, No. 1, Pp.19-53, 2021.
[5] Kaur, Navneet., “PLDD Using Ensemble Classification and Feature Extraction,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol.12, No. 11, Pp.2339-2352, 2021.
[6] Deepalakshmi, P., K. Lavanya, and Parvathaneni Naga Srinivasu, “PLDD Using CNN Algorithm,” International Journal of Information System Modeling and Design (IJISMD), Vol.12, No. 1 , Pp.1-21, 2021.
[7] Chowdhury, Muhammad EH, Tawsifur Rahman, Amithkhandakar, Mohamed Arseleneayari, Aftabullah Khan, Muhammad Salman Khan, Nasser Al-Emadi, Mamun Bin Ibnereaz, Mohammad Tariqul Islam, and Sawal Hamid Md Ali, “Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques,” Agriengineering, Vol. 3, No. 2 , Pp.294-312, 2021.
[8] Abbas, Amreen, Sweta Jain, Mahesh Gour, and Swethavankudothu, “Tomato Plant Disease Detection Using Transfer Learning With C-GAN Synthetic Images, Computers and Electronics In Agriculture,” Vol. 187, Pp.106279, 2021.
[9] Lu, Jinzhu, Lijuan Tan, and Huanyu Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, Vol. 11, No. 8 , Pp.707, 2021.
[10] Mohanty, Sharada P., David P. Hughes, and Marcel Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers In Plant Science , Vol.7 , Pp.1419, 2016.
[11] Sladojevic, Srdjan, Marko Arsenovic, Andrasanderla, Dubravkoculibrk, and Darkostefanovic, “Deep Neural Networks Based Recognition of Plant Diseases By Leaf Image Classification,” Computational Intelligence and Neuroscience, Vol.2016, 2016.
[12] Ramcharan, Amanda, Kelseebaranowski, Peter Mccloskey, Babuali Ahmed, James Legg, and David P. Hughes, “Deep Learning for Image-Based Cassava Disease Detection,” Frontiers In Plant Science, Vol.8 , Pp.1852, 2017.
[13] Fuentes, Alvaro, Sook Yoon, Sang Cheol Kim, and Dong Sun Park, “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition,” Sensors, Vol. 17, No. 9, Pp.2022, 2017.
[14] Ferentinos, Konstantinos P, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics In Agriculture, Vol.145 , Pp.311-318, 2018.
[15] Agarwal, Mohit, Abhishek Singh, Siddhartha Arjaria, Amit Sinha, and Suneet Gupta, “Toled: Tomato Leaf Disease Detection Using Convolution Neural Network,” Procedia Computer Science, Vol.167, Pp.293-301, 2020.
[16] Karthik, R., M. Hariharan, Sundaranand, Priyanka Mathikshara, Annie Johnson, and R. Menaka, “Attention Embedded Residual CNN for Disease Detection In Tomato Leaves,” Applied Soft Computing, Vol.86 , Pp.105933, 2020.