Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies

Espejo-García, Borja; Malounas, Ioannis; Mylonas, Nikos; Kasimati, Aikaterini; Fountas, Spyros

doi:10.1016/j.compag.2022.106868

Cited by 40 publications

(15 citation statements)

References 25 publications

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“…Given that images from different domains contain common underlying features among them, transfer learning makes the training more stable by transferring knowledge of common features in the convolutional layer, thus improving the training efficiency ( Espejo-Garcia et al, 2022 ; Zhao X. et al, 2022 ). Inspired by this, this study is based on transfer learning to train the EfficientNet-B7-CBAM network.…”

Section: Methodsmentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.

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Section: Methodsmentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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“…Spectral differences in the images responded to the effect of fertilizer dosage on broccoli growth status with a recognition accuracy of 98.65%. Espejo-Garcia and others 15 used a deep neural network and transfer learning to identify the degree of nutrient deficiency symptoms in RGB images. They used the EfficientNetB4 network to train images of sugar beets deficient in nitrogen (N), phosphorus (P), and kalium (K) micronutrients to monitor the growth status of the beet.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based method for real-time spinach seedling health monitoring

Xu,

Cong,

Zhai

et al. 2024

J. Electron. Imag.

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It is difficult to obtain real-time crop water shortage during spinach seedling production, so this study proposes a real-time spinach seedling water stress classification method based on hierarchical transformer encoder (HTE) MobileNet (HT-MobileNet). An efficient sandglass residual module is designed for the grading algorithm to significantly improve the model grading accuracy. Meanwhile, a lightweight attention mechanism is proposed. It can effectively suppress the influence of irrelevant features on the classification results. Furthermore, an HTE is introduced, classifying the degree of stress accurately under extremely complex spinach seedling conditions. By introducing grouped convolution, the number of model parameters are reduced greatly, which lays the foundation for practical application. The grading accuracy is 94.61%. The number of parameters is 1.78M and the model size is 4.57MB, which are 40.67% lower than the original network. Comparison experiments are conducted between HT-MobileNet and 10 mainstream convolutional neural networks, such as ConvNext. The experimental results demonstrate the outstanding advantages of this research in terms of accuracy and weight. In addition, an intelligent spinach seedling water stress grading system is designed and tested. This study can provide technical support for real-time monitoring of spinach seedling growth status.

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“…An insufficient supply of nitrogen will result in certain morphological traits such as decreased leaf area, foliage discoloration, and plant dwarf, or physiological characteristics such as weakened photosynthesis, respiration, and other activities. Under nutrient deficiency conditions, different transcription factors and regulatory gene networks function together to maintain mineral homeostasis [ 108 , 109 ]. For more detail on the nutrient deficiency stress response, see [ 100 ].…”

Section: Root View Under Abiotic Stressmentioning

confidence: 99%

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

Wu,

Shao,

et al. 2024

Plant Phenomics

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The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

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Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies

Cited by 40 publications

References 25 publications

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Deep learning-based method for real-time spinach seedling health monitoring

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

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