Plant Disease Detection using DenseNet169

DOI

10.2991/978-94-6463-716-8_2How to use a DOI?

Keywords

Convolutional Neural Network; DenseNet169; Image Processing; Computer Vision; Detection of Plant Disease

Abstract

Infected plants have a great deal of impact on any country’s economy. Normally, farmers and agricultural professionals keep a keen eye on these crops for detection of this disease. However, this process is often time-consuming, very tedious and almost imperfect. The growth of plants and their well-being is very important for farmers’ growth, it directly affects their economy too. Traditionally, plant disease detection is carried out by observing various spots on affected plants. The main objective of this study is to implement a robust model for recognition of diseases which classifies disease on the basis of leaf images. Convolutional neural network algorithm called DenseNet169, is used to recognize plant diseases also with the help of Plant Village Dataset taken from TensorFlow. A convolutional neural network (CNN) is a type of neural network that’s usually accustomed to analyzing pictures. It consists of numerous layers, every of which performs an operational convolution on the input data (thus the name “convolutional”) Thismethod is segregated into two phases. In the first phase, the input image is loaded, and segmentation algorithms are applied to detect parts of the plant that have been affected by diseases. To extract features from CNN models, we use Feature Extraction. Afterwards, we need to train the CNN network with the last sigmoid/logistic dense layer with respect to the target variable.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ayesha Butalia
AU  - Pranav Gaikwad
AU  - Pushkar Kumar
PY  - 2025
DA  - 2025/05/26
TI  - Plant Disease Detection using DenseNet169
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 7
EP  - 24
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_2
DO  - 10.2991/978-94-6463-716-8_2
ID  - Butalia2025
ER  -