Determination of Leaf Water Content with a Portable NIRS System Based on Deep Learning and Information Fusion Analysis

Zhou, Lei; Zhang, Chu; Taha, Mohamed Farag; Qiu, Zhengjun; He, Yong

doi:10.13031/trans.13989

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…This results in researchers exploring new methods to improve NIR in this application. Other application 'hot spots' include grain, 28,37,[48][49][50][51][52] organic matter such as leaves, wood and beans, [53][54][55][56][57][58][59] food powders, 37,60,61 oil 62 and brain, 63 with 7, 7, 3, one and one papers, respectively.…”

Section: Cnn For Nir Spectroscopymentioning

confidence: 99%

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Walsh

Neupane

Koirala

et al. 2023

Journal of Near Infrared Spectroscopy

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The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral pre-processing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.

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Section: Cnn For Nir Spectroscopymentioning

confidence: 99%

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Walsh

Neupane

Koirala

et al. 2023

Journal of Near Infrared Spectroscopy

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show abstract

“…Many studies have reported that abundant, diverse, and well-proportioned samples can significantly improve the efficiency of models in spectroscopic analysis, both qualitatively and quantitatively [1][2]. Moreover, with the emergence of the big data era, deep learning (DL) algorithms have been widely applied to spectral analysis, posing a greater challenge to the number of spectral samples due to the data sensitivity of these algorithms [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

AE-BEGAN based Synthetic Data Augmentation for Sample-Limited High-Dimensional Problems with Application to NIR Spectral Data

Luo,

Fan,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Synthetic data augmentation holds substantial research and application value in scenarios characterized by limited samples and high dimensions. It enhances the analytical ability and efficiency of spectral analysis models. This paper proposes Autoencoder-Combined Boundary Equilibrium Generative Adversarial Networks (AE-BEGAN) as a new method for augmenting synthetic data in scenarios with limited samples and high dimensions, with a specific emphasis on near-infrared (NIR) spectral data. The spectral data first undergoes preprocessing procedures that encompass advanced noise reduction algorithms and techniques for removing abnormal samples, guaranteeing elimination of unwanted disturbances and outliers. Then, the pre-processed data is utilized to train the AE-BEGAN model, which generates augmented synthetic samples. Finally, real NIR spectral data obtained from lubricant samples exhibiting different water contents were employed to validate and test the performance of the model. The experimental results demonstrate that the AE-BEGAN model outperforms other GANs in generating synthetic data of high quality and diversity, as quantified by two evaluation metrics, α-Precision and β-Recall with scores of approximately 0.86 and 0.28, respectively. The application case study confirms that the AE-BEGAN model exhibits the capability to generate derived NIR spectra and expand the number of spectra in scenarios with limited samples and high dimensions.

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“…As a subclass of machine learning methods, deep learning (DL) models have recently realized 'success in the field of plant nutrient status diagnosis, which refers to using deep neural networks that include a pretty large number of processing layers to analyze and process data. Since the early 2000s, DCNNs have been utilized for analyzing RGB images [25], such as images segmentation of biological materials [26], recognition of plants [27], prediction of leaf water content [28] and plant diseases detection [29]. Additionally, DCNNs automatically learn and extract the most descriptive features from the images during the training process, which thoroughly addresses the problems of hand-crafted features [30].…”

Section: Introductionmentioning

confidence: 99%

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

Self Cite

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In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

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Determination of Leaf Water Content with a Portable NIRS System Based on Deep Learning and Information Fusion Analysis

Cited by 17 publications

References 26 publications

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

AE-BEGAN based Synthetic Data Augmentation for Sample-Limited High-Dimensional Problems with Application to NIR Spectral Data

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

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