Rice Nitrogen Nutrition Diagnosis System based on RGB Images and CNN Model
Nitrogen nutrition diagnosis is one of the key technologies to achieve high yield and quality in rice cultivation.In order to quickly obtain the nitrogen nutrition status and fertilization prescription of rice by using unmanned aerial vehicles(UAVs)or smartphones to collect rice canopy images,a 7-level nitrogen fertilization experiment was conducted in purple rice fields from 2021 to 2022,and rice canopy UAV RGB images with different nitrogen contents were obtained.A convolutional neural network(CNN)optimized model was obtained through deep learning methods to construct a rice nitrogen nutrition diagnosis system based on Android smartphones.Results showed that the impact of different nitrogen levels on rice leaf nitrogen content was more obvious in the tillering and panicle initiation stages.A total of 10 173 RGB canopy images were obtained.By adjusting the CNN training parameters such as batch_size,epoch,learning_rate,and image scaling ratio,a total of nine models with accuracy exceeding 80% were obtained.A set of APP client programs based on Android smartphones was developed to achieve the migration of deep learning models from the Python environment to the Android system.By utilizing UAVs or smartphones to collect RGB canopy images of rice at different growth stages,the rice nitrogen nutrition diagnosis system based on Android smartphones constructed by deep learning CNN models is feasible and can be used as a tool for rapidly diagnosing nitrogen nutrition during the rice growth period.
国家科技期刊平台
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