Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks
2. Materials and Methods
2.1. Obtaining Micrographs
- Pumpkin fruits (Cucurbita pepo L.) were collected and stored at 15–20 C. Cylinders (25 mm length, 15 mm diameter) from the mesocarp’s middle zone, parallel to the fruit’s major axis, were taken.
- A rectangular slab of 0.5–1.0 mm of thickness was gently cut parallel to cylinder’s height at the maximum section area. The slab was then divided into four symmetrical cuts, and each quarter was newly divided into six parts.
- These parts were fixed in 2.5% glutaraldehyde in 1.25% PIPES buffer at pH 7.0–7.2 during 24 h at room temperature. The parts were then dehydrated in a water/ethanol series and embedded in LR White resin (London Resin Co., Basingstoke, UK). After the samples were embedded in resin, semi-thin sections (0.6 μm) of the resin blocks were obtained with a microtome (model Reichert-Supernova, Leica, Wien, Austria).
- The sections were stained with an aqueous solution Azure II 0.5%, Methylene Blue 0.5%, Borax 0.5% during 30 s. They were then washed in distilled water and mounted on a glass slide.
- Micrographs of the stained samples were obtained under a stereomicroscope (Olympus SZ-11, Tokyo, Japan) that was attached to a digital color video camera (SONY SSC-DC50AP, Tokyo, Japan) and a computer.
2.2. Digital Treatment of the Micrographs
2.2.1. Improvement and Enhancement
2.2.2. Image Processing
2.3. Data Extraction
2.4. Radial Basis Neural Network—RBNN
2.5. Convolutional Neural Network—CNN
2.6. Learning Transfer
2.7. Statistical Comparison of Models
- Accuracy: This measures how many observations, both positive and negative, were correctly classified and it is defined by Equation (7).
- Recall: This measures how many observations out of all of the positive observations were classified as positive (see Equation (8)).
- Precision: This measures how many observations predicted as positive are, in fact, positive (see Equation (9)).
- F1–score: This combines precision and recall into one metric (harmonic mean, see Equation (10)).
3. Results and Discussions
3.1. Digital Micrograph Processing
3.2. Microstructural Elements
3.3. CNN Implementation
3.4. Statistical Analysis
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Oblitas, J.; Mejia, J.; De-la-Torre, M.; Avila-George, H.; Seguí Gil, L.; Mayor López, L.; Ibarz, A.; Castro, W. Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks. Appl. Sci. 2021, 11, 1581. https://doi.org/10.3390/app11041581
Oblitas J, Mejia J, De-la-Torre M, Avila-George H, Seguí Gil L, Mayor López L, Ibarz A, Castro W. Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks. Applied Sciences. 2021; 11(4):1581. https://doi.org/10.3390/app11041581Chicago/Turabian Style
Oblitas, Jimy, Jezreel Mejia, Miguel De-la-Torre, Himer Avila-George, Lucía Seguí Gil, Luis Mayor López, Albert Ibarz, and Wilson Castro. 2021. "Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks" Applied Sciences 11, no. 4: 1581. https://doi.org/10.3390/app11041581