Nutrient Deficiency Diagnosis of Plants Based on Transfer Learning and Lightweight Convolutional Neural Networks MobileNetV3-Large

Abstract

Nutrient Deficiency Diagnosis of Plants is an important application in precision agriculture. At present, nutrient deficiency diagnosis of plants mainly depends on manual identification, which makes it difficult to ensure efficiency and accuracy. Therefore, based on deep learning and focusing on the problems of difficult convergence and poor real-time performance of the existing deep convolution neural network in the detection of plant nutrient deficiency, this study proposes a lightweight model—UMNet (Nutrient-MobileNetV3-Network) for plant nutrient deficiency detection. This model enhances the collected rice leaf images to expand the dataset, then migrates the knowledge learned by the MobilenetV3-Large network on the ImageNet dataset to UMNet, redesigns a new full connection layer, and uses a new activation function. The experimental results show that: (1) Transfer learning solves the problem of insufficient training data. Compared with learning without transfer learning, the accuracy increases by 7.22% ∼ 9.63%, which greatly improves the convergence speed and recognition accuracy of the model. (2) Compared with complex convolutional neural networks(CNN), such as InceptionV3, InceptionResnetV2 and VGG16, the lightweight network UMNet has lower storage requirements and shorter training time. At the same time, it can still ensure high accuracy, and the recognition accuracy is better than other lightweight networks with the same complexity: ShuffleNetV2, EfficientNetB0 and Xception. The identification accuracy of the plant nutrient deficiency detection model UMNet constructed in this paper can reach 97.80%, and the training time of a single epoch is about 46.4s. It only takes 1.45s to predict the nutrient deficiency of a single object, which realizes the intelligent detection in the field of plant nutrient deficiency, and it will promote academic exploration of deep learning in plant phenotype research.