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An electrical impedance spectroscopy (EIS) experiment was performed using a double-shell electrical model to investigate the feasibility of detecting physiological changes in lettuce leaves over 16 h. Four lettuce plants were used, and the impedance spectra of the leaves were measured five times per plant every hour at frequencies of 500 Hz and 300...
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Context 1
... leaf cells can exist independently as basic structural and functional units of life [33]. Figure 2 presents the simplified anatomy of a plant cell. The cell wall surrounds the cell membrane, which is made up of two layers of phospholipids and proteins. ...
Context 2
... cell can be considered a conductor surrounded by an insulating envelope containing a substructure with similar properties. Figure 2 shows a cross-section of a single plant cell. A wide range of electrical models for plant tissue impedance spectra have been presented by numerous researchers [21][22][23]34]. ...
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Electrical impedance spectroscopy (EIS) has been suggested as a possible technique to differentiate between thyroid and parathyroid tissue during surgery. This study aims to explore this potential using computational models to simulate the impeda...
Citations
When it comes to growing lettuce, specific nutrients play vital roles in its growth and development. These essential nutrients include full nutriments (FN), nitrogen (N), phosphorus (P), and potassium (K). Insufficient or excess levels of these nutrients can have negative effects on lettuce plants, resulting in various deficiencies that can be observed in the leaves. To better understand and identify these deficiencies, a deep learning approach is employed to improve these tasks. For the study, YOLOv8 Nano, a lightweight deep network, is chosen to classify the observed deficiencies in lettuce leaves. Several enhancements to the baseline algorithm are made, the backbone is replaced with VGG16 to improve the classification accuracy, and depthwise convolution is incorporated into it to enrich the features while keeping the head unchanged. The proposed network, incorporating these modifications, achieved superior classification results with a top-1 accuracy of 99%. This performance outperformed other state-of-the-art classification methods, demonstrating the effectiveness of the approach in identifying lettuce deficiencies. The objective of the research was to improve a baseline algorithm that could achieve the classification task above 85% of top-1 accuracy, with a FLOP inferior to 10G, and classification latency below 170 ms per image.
Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.
