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- W4200468247 abstract "Crop yield depends on various parameters like the type of soil, irrigational water, environmental conditions, nutritional contents of the plants, and etc. In India, farmers are facing many problems due to improper nutrition management for receiving a better crop yield in groundnut crop. This work deals with nutrition management in groundnut crop through proper detection of nutrition deficiency, based on identified nutrient deficiency predicting the crop yields. Now a days, most of the research on it considers only primary nutrients, whereas few others are considering micro nutrients deficiency. But there is no specific nutrient deficiency method available for finding primary and micro nutrient deficiencies at present. Though they consider, there is no exclusive measurements available for knowing deficiency percentage. Also, accuracy is not in acceptable level for their work. The proposed work focuses on finding primary (Nitrogen, Potassium, Phosphorus) and micro (Zinc) nutrient deficiencies. For experimentation, ESP32 camara images that are captured from crop field are used. Visual geometry group (VGG16) convolutional neural network architecture was used for classification of images and the K-Means clustering algorithm is used for the estimation of nutrient deficient percentage." @default.
- W4200468247 created "2021-12-31" @default.
- W4200468247 creator A5066786788 @default.
- W4200468247 creator A5089085969 @default.
- W4200468247 date "2021-10-25" @default.
- W4200468247 modified "2023-09-26" @default.
- W4200468247 title "An IoT Framework for Groundnut Crop Yield Prediction Using K-Means Algorithm" @default.
- W4200468247 doi "https://doi.org/10.1109/icdabi53623.2021.9655923" @default.
- W4200468247 hasPublicationYear "2021" @default.
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