Editorial
Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey
@ARTICLE{10.4108/eetiot.4595, author={Amol Bhilare and Debabrata Swain and Niraj Patel}, title={Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={12}, keywords={Plant diseases, Transfer learning, Densenet, DN, Efficientnet, EN, Convolutional Neural Network, CNN, Resnet, RN}, doi={10.4108/eetiot.4595} }- Amol Bhilare
Debabrata Swain
Niraj Patel
Year: 2023
Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey
IOT
EAI
DOI: 10.4108/eetiot.4595
Abstract
Agriculture plays an important role towards the economic growth of any nation. It also has a significant effect on global GDP. The enhancement in agro production helps in controlling greatly the inflation. Today a large percentage of population from rural India is still dependent on agriculture. But every year there is a huge loss happen in agriculture due to different plant diseases. A farmer does not able to recognise any plant disease at its beginning stage due to insufficient knowledge. Sometimes they take help of agriculture officers in this process. However, if the infection level has grown by that point, it typically leads to a significant crop loss. Also the diagnosis made by the agriculture officer based on their past experience, is always not accurate. Computational vision-based solutions can be used to deal with this great disaster to a large extent. Computer vision mainly deals with different algorithms that enable a computer to identify a hidden pattern for recognition using image or video data. In this work a detailed investigation has been performed on the different computer vision based solutions proposed by different authors to detect various crop diseases.
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