Agriculture has always been an important economic and social sector for humans. Fruit production is especially essential, with a great demand from all households. Therefore, the use of innovative technologies is of vital importance for the agri-food sector. Currently artificial intelligence is one very important technological tool widely used in modern society. Particularly, Deep Learning (DL) has several applications due to its ability to learn robust representations from images. Convolutional Neural Networks (CNN) is the main DL architecture for image classification. Based on the great attention that CNNs have had in the last years, we present a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection. We observe that in the last two years (2019–2020), the use of CNN for fruit recognition has greatly increased obtaining excellent results, either by using new models or with pre-trained networks for transfer learning. It is worth noting that different types of images are used in datasets according to the task performed. Besides, this article presents the fundamentals, tools, and two examples of the use of CNNs for fruit sorting and quality control.
Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.
INTRODUCCIÓN. La formación en emprendimiento y su impacto en la intención por emprender en universitarios es una temática que ha cobrado relevancia durante los últimos años. El objetivo de este estudio es analizar la relación entre la formación emprendedora y la intención por emprender. MÉTODO. Para ello, se aplicó un instrumento de medición en una muestra de 286 estudiantes universitarios chilenos. En el procesamiento estadístico, se validó psicométricamente el constructo basado en la Teoría del Comportamiento Planificado (TCP) y se utilizaron estadísticas inferenciales para analizar la existencia de diferencias entre la intención emprendedora y las modalidades de programas de formación en emprendimiento. RESULTADOS. Los resultados muestran que existen diferencias positivas atribuibles a la educación emprendedora. Así, el haber cursado asignaturas relacionadas al emprendimiento muestra diferencias significativas sobre la intención por emprender, las actitudes empresariales y la capacidad percibida del estudiantado. También, se encontró que el participar de experiencias prácticas en emprendimiento muestra diferencias significativas en todas las dimensiones de la TCP por lo que parecen ser eficaces en la modificación de la conducta por emprendimiento en los estudiantes. DISCUSIÓN. Se discuten las implicancias de los resultados, en términos de la importancia de la formación práctica y la oferta de cursos adicionales al curriculum obligatorio en emprendimiento. Finalmente, como líneas futuras de investigación se considera pertinente explorar las dimensiones de actitud y autoeficacia emprendedora, considerando el impacto que tiene la formación en emprendimiento sobre estas variables.
La pandemia por COVID-19 ha traído grandes consecuencias económicas en los mercados. Este trabajo analiza la relación entre el avance de programas de vacunación y mercados financieros latinoamericanos. Se utiliza un enfoque de análisis de coherencia Wavelet para evaluar el movimiento conjunto de los mercados y el avances de estrategias de inoculación en base a datos diarios de Argentina, Brasil, Chile y México. Los resultados muestran que el avance de los programas de vacunación en los países latinoamericanos tienen efectos positivos y significativos en los rendimientos de sus mercados financieros.
This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 × 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.
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