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- W4386960852 abstract "The nutrient element content of plants is one of the important factors affecting the growth and yield of crops. The use of the convolutional neural network (CNN) can quickly and accurately detect the degree of nutrient deficiency in plants, thereby liberating manpower and improving crop yields. This study uses MobileNetV3-Large as the backbone, combines a convolutional block attention module (CBAM) to obtain MobileNetV3-CBAM, and introduces gated linear units (GLU). During the training process, fine-tuning of transfer learning improves the speed and accuracy of model training. The identification of elements deficient in plants is studied using an open-source dataset, and compared with the cutting-edge CNN, the proposed lightweight MobileNetV3-CBAM with GLU has better comprehensive performance: I. For the imbalanced dataset, the proposed MobileNetV3-CBAM model achieves outstanding results in classifying plant nutrient deficiencies, with a remarkable test accuracy of 96.54%. II. The proposed model occupies a small memory (10.5M), and the training speed is remarkable, which can realize the identification of plant nutrient deficiency in a low-computing environment. III. After 10-fold cross-validation, the robustness of the model is good. This study can provide a theoretical basis and technical support for the real-time detection of nutrient deficiency in plant leaves." @default.
- W4386960852 created "2023-09-23" @default.
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- W4386960852 date "2023-01-01" @default.
- W4386960852 modified "2023-09-29" @default.
- W4386960852 title "Identification of Plant Nutrient Deficiency Based on Improved MobileNetV3-Large Model" @default.
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- W4386960852 doi "https://doi.org/10.1007/978-981-99-6187-0_40" @default.
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