Olive Fruit Selection through AI Algorithms and RGB Imaging

A prediction model based on random survival forest analysis of the overall survival of elderly female papillary thyroid carcinoma patients: a SEER-based study

OBJECTIVE

Papillary thyroid carcinoma (PTC) is a common malignancy whose incidence is three times greater in females than in males. The prognosis of ageing patients is poor. This research was designed to construct models to predict the overall survival of elderly female patients with PTC.

METHODS

We developed prediction models based on the random survival forest (RSF) algorithm and traditional Cox regression. The data of 4539 patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Twelve variables were analysed to establish the models. The C-index and the Brier score were selected to evaluate the discriminatory ability of the models. Time-dependent receiver operating characteristic (ROC) curves were also drawn to evaluate the accuracy of the models. The clinical benefits of the two models were compared on the basis of the DCA curve. In addition, the Shapley Additive Explanations (SHAP) plot was used to visualize the contribution of the variables in the RSF model.

RESULTS

The C-index of the RSF model was 0.811, which was greater than that of the Cox model (0.781). According to the Brier score and the area under the ROC curve (AUC), the RSF model performed better than the Cox model. On the basis of the DCA curve, the RSF model demonstrated fair clinical benefit. The SHAP plot showed that age was the most important variable contributing to the outcome of PTC in elderly female patients.

CONCLUSIONS

The RSF model we developed performed better than the Cox model and might be valuable for clinical practice.

Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models

During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer's decision-making process through further automatic applications.

Use of Artificial Vision during the Lye Treatment of Sevillian-Style Green Olives to Determine the Optimal Time for Terminating the Cooking Process

This study focuses on characterizing the temporal evolution of the surface affected by industrial treatment with NaOH within the processing tanks during the lye treatment stage of Manzanilla table olives. The lye treatment process is affected by multiple variables, such as ambient temperature, the initial temperature of the olives before lye treatment, the temperature of the NaOH solution, the concentration of the solution, the variety of olives, and their size, which are determinants of the speed of the lye treatment process. Traditionally, an expert, relaying on their subjective judgement, manages the cooking process empirically, leading to variability in the termination timing of the cook. In this study, we introduce a system that, by using an artificial vision system, allows us to know in a deterministic way the percentage of lye treatment achieved at each moment along the cooking process; furthermore, with an interpolator that accumulates values during the lye treatment, it is possible to anticipate the completion of the cooking by indicating the moment when two-thirds, three-fourths, or some other value of the interior surface will be reached with an error of less than 10% relative to the optimal moment. Knowing this moment is crucial for proper processing, as it will affect subsequent stages of the manufacturing process and the quality of the final product.

Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks

To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R² values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R², other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.