Plant Diseases Concept in Smart Agriculture Using Deep Learning

Author:

Chauhan Prachi1,Mandoria Hardwari Lal1,Negi Alok2,Rajput R. S.1

Affiliation:

1. Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India

2. National Institute of Technology, Uttarakhand, India

Abstract

In the agricultural sector, plant leaf diseases and harmful insects represent a major challenge. Faster and more reliable prediction of leaf diseases in crops may help develop an early treatment technique while reducing economic losses considerably. Current technological advances in deep learning have made it possible for researchers to improve the performance and accuracy of object detection and recognition systems significantly. In this chapter, using images of plant leaves, the authors introduced a deep-learning method with different datasets for detecting leaf diseases in different plants and concerned with a novel approach to plant disease recognition model, based on the classification of the leaf image, by the use of deep convolutional networks. Ultimately, the approach of developing deep learning methods on increasingly large and accessible to the public image datasets provides a viable path towards massive global diagnosis of smartphone-assisted crop disease.

Publisher

IGI Global

Reference25 articles.

1. Phoneme classification with bidirectional LSTM and other neural network architectures.;GAlex;Neural Networks,2005

2. Convolutional neural networks for the automatic identiðcation of plant diseases.;Boulent;Frontiers in Plant Science,2019

3. Tropical agriculture and global warming: impacts and mitigation options

4. Climate change: potential impact on plant diseases

5. CLIMATE CHANGE AND PLANT DISEASE MANAGEMENT

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ResNet-Based Classification for Leaf Disease Detection;Journal of The Institution of Engineers (India): Series B;2024-05-17

2. Video summarization using deep learning techniques: a detailed analysis and investigation;Artificial Intelligence Review;2023-03-15

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