Rice Disease Detection Using DenseNet and Convolutional Neural Network
Mohammed Nabeel
, Mohamed Ridwan Nisath S , Rohan Vignesh S
visual computing,neural networks,architectures,GoogleNet,ResNet-18, SqueezeNet-1.0, and DenseNet-121
In the research, we investigate the viability of applying machine learning and visual computing to identify various fungal infections of rice. Brown spot and leaf blast illnesses are taken into account. To recognise a specific disease on an image, modern computer vision techniques based on convolutional neural networks are employed. The four most popular and compact convolutional neural network architectures—GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121—are compared by the authors. The authors demonstrate that the disease can be identified with at least 95% accuracy in the dataset utilized for the investigation. Testing the algorithm on real data that wasn't used for training revealed up to 95.6% accuracy. This is a good sign of the solution's dependability and stability, even when the data distribution changes. The data which is not used in the training gives the accuracy of 95.6%. This is a good sign of the solution's dependability and stability, even when the data distribution changes.
"Rice Disease Detection Using DenseNet and Convolutional Neural Network", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.737-742, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001117.pdf
Volume 11
Issue 2,
February-2024
Pages : 737-742
Paper Reg. ID: TIJER_151016
Published Paper Id: TIJERB001117
Downloads: 000276
Research Area: Science and Technology
Country: TIRUVALLUR, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
