Different Crop Leaf Disease Detection Using Convolutional Neural Network
- DOI
- 10.2991/978-94-6463-136-4_85How to use a DOI?
- Keywords
- Convolutional Neural Network; Crop Disease Detection; Image Analytics
- Abstract
Crop diseases are a considerable danger to the crop’s health, affecting the yield. Timely detection is challenging due to a lack of infrastructure in many regions of the world. Since they result in the death of plants, the loss of their product, and the global food problem, plant diseases must be investigated. Crop disease detection has been made possible by recent advancements in computer vision, deep learning, and the growing worldwide adoption of smartphones. Convolutional Neural Networks have significantly improved classifying images in the past several years. The performance of deep learning-based techniques for plant disease recognition under actual circumstances is thoroughly examined in this research. The objective was to offer some principles for conducting a more thorough and realistic examination of deep learning-based approaches for disease recognition. Sequential Architecture was used to classify 38 diseases of 14 crops on a crop leaves image dataset containing 70,295 training and 17,572 testing images. A simple convolutional neural network has been proposed that detects crop diseases seamlessly. The maximum accuracy obtained was 95% on the 14th epoch. This was accomplished by following the Sequential Model. It is a cutting-edge network that can help new researchers who desire to conduct their studies in deep learning applications with an emphasis on agriculture.
- Copyright
- © 2023 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.
Cite this article
TY - CONF AU - Ashutosh Pawar AU - Mihir Singh AU - Swapnil Jadhav AU - Vidya Kumbhar AU - T. P. Singh AU - Sahil K. Shah PY - 2023 DA - 2023/05/01 TI - Different Crop Leaf Disease Detection Using Convolutional Neural Network BT - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) PB - Atlantis Press SP - 966 EP - 979 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-136-4_85 DO - 10.2991/978-94-6463-136-4_85 ID - Pawar2023 ER -