Classification of pest detection in paddy crop based on transfer learning approach
Malathi.V
, Sarath Chandra Reddy.T , Vishal.M , Sanjay.S , Sanjay.R
deep learning neural network.
For farmers, spotting pests in the agricultural sector is a crucial problem that restricts economic expansion. Farmers have up until now increased yield production by adhering to traditional procedures. Investigators have used deep learning to genuinely categorise types of photos. In this study, ten different types of pathogens found in the rice production are identified using deep convolutional neural networks (DCNN). Given that deep learning requires more data sets, the data structure contains about 3549 pest photos that threaten rice crops. This technique is referred to as data reinforcement.The Neural network was constructed using a variety of DCNN topologies, and performance and accuracy rates of the models were examined. The word embedding technique is then applied to the pest data set by adjusting the hyperparameters and stages of such ResNet-50 model. The refined ResNet-50 model beat other models in terms of precision, scoring 95.012%, according to a comparison of the resulting value. The model's effectiveness in categorizing pest diseases is shown in the derived outcome value.
"Classification of pest detection in paddy crop based on transfer learning approach", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.485-489, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001076.pdf
Volume 11
Issue 2,
February-2024
Pages : 485-489
Paper Reg. ID: TIJER_151035
Published Paper Id: TIJERB001076
Downloads: 000295
Research Area: Science and Technology
Country: thiruvallur ,chennai, 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
