DenseNet Based Model for Plant Diseases Diagnosis


  •   Mahmoud Bakr

  •   Sayed Abdel-Gaber

  •   Mona Nasr

  •   Maryam Hazman


The biggest threat to the safety of food is plant diseases. They have the ability to dramatically lower the quantity and quality of agricultural products. Recognizing plant diseases is the biggest issue in the agricultural industries. Convolutional Neural Networks (CNN) are effective in solving image classification problems in computer vision. Numerous deep learning architectures have been used to diagnose plant diseases. This study presents a transfer learning-based model for identifying diseases in plant leaves. In this paper, a CNN classifier based on transfer learning model called DenseNet201 are proposed. An analysis of four deep learning models (VGG16, Inception V3, ResNet152V2, and DenseNet201) done to see which one can detect plant diseases with the greatest degree of accuracy. Web based application developed for plant disease diagnosing from defected leaf image and the proposed model which identify the disease and give the recommended treatment. The used images dataset contains 28310 leaves photos of 3 crops, tomato, potato and pepper divided into 15 different classes, 9 disorders and one healthy class for tomato, 2 disorders and one healthy class for potato and 1 disorder and one healthy for pepper. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.44% and validation accuracy of 98.70%.

Keywords: Convolutional Neural Network, Deep Learning, Leaf Disease Detection, Transfer Learning.


Plant health and food security [Internet]. FAO; 2017. Available from:

Mohamed A, Abdel-Gaber S, Nasr M, Hazman M. An Intelligent Approach to Mitigate Effects of Climate Change and Insects on Crops. International Journal of Computer Science and Information Security (IJCSIS). 2020; 18(3).

Venkatesh, Y N, S ST, S S, Hegde SU. Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose. In: 2020 IEEE International Conference for Innovation in Technology (INOCON). 2020: 1–7.

Peyal HI, Shahriar SM, Sultana A, Jahan I, Mondol MdH. Detection of Tomato Leaf Diseases Using Transfer Learning Architectures: A Comparative Analysis. In: 2021 International Conference on Automation, Control and Mechatronics for Industry 40 (ACMI). 2021: 1–6.

kaggle [Internet]. 2018. Available from:

Hong H, Lin J, Huang F. Tomato Disease Detection and Classification by Deep Learning. In: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). 2020: 25–9.

Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network. Procedia Computer Science. 2020;167:293–301.

Kabir MM, Ohi AQ, Mridha MF. A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network. CoRR [Internet]. 2020;abs/2011.05151. Available from:

Rangarajan AK, Purushothaman R, Ramesh A. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Computer Science. 2018;133:1040–7.

Ji M, Zhang L, Wu Q. Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Information Processing in Agriculture. 2020;7(3):418–26.

Afifi A, Alhumam A, Abdelwahab A. Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data. Plants [Internet]. 2021;10(1). Available from:

Saleem MH, Potgieter J, Arif KM. Plant Disease Detection and Classification by Deep Learning. Plants [Internet]. 2019;8(11). Available from:

Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture. 2020;173:105393.

Shorten C, Khoshgoftaar TM. A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data. 2019 Jul 6;6(1):60.

Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. 2019;161:272–9.

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In: Bengio Y, LeCun Y, editors. 3rd International Conference on Learning Representations, ICLR. 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings [Internet]. 2015. Available from:

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. p. 2818–26.

Gulli A, Pal S. Deep Learning with Keras. Birmingham, United Kingdom: Packt; 2017: 318.

Kumar V, Arora H, Harsh, Sisodia J. ResNet-based approach for Detection and Classification of Plant Leaf Diseases. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). 2020. p. 495–502.

Huang G, Liu Z, Weinberger KQ. Densely Connected Convolutional Networks. CoRR [Internet]. 2016;abs/1608.06993. Available from: [Internet]. 2021. Available from:

Aquiles C. How to use HDF5 files in Python [Internet]. python for the lab. [cited 2022 Aug 1]. Available from:


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How to Cite
Bakr, M., Abdel-Gaber, S., Nasr, M. and Hazman, M. 2022. DenseNet Based Model for Plant Diseases Diagnosis. European Journal of Electrical Engineering and Computer Science. 6, 5 (Sep. 2022), 1–9. DOI: