Technologies applied to characterise and improve the quality and traceability of table olives at harvest and post-harvest

La presente tesis doctoral aborda el estudio de las nuevas tecnologías para su aplicación a la mejora de la calidad y trazabilidad de la aceituna de mesa en la operación de cosecha y postcosecha. Se presenta un estudio donde se analizan medios de recolección y recepción habituales respecto de la caracterización del molestado que generan al fruto. Además, se expone una solución para el análisis del molestado del fruto en toda su superficie. Seguidamente se propone un monitor de rendimiento desarrollado mediante tecnología Time of Flight (ToF) para la estimación del peso del fruto cosechado y almacenado a partir del volumen ocupado por este. El monitor de rendimiento expuesto es comparado respecto de los sistemas de pesado actuales basados en células de carga. Otro tema tratado es la realización de lotes de fruto de una calidad determinada a pie de campo mediante un prototipo desarrollado basado en un remolque que integra funciones de limpieza y clasificación de fruto. En esta última operación se realiza la evaluación de parámetros de molestado, calibre y grado de madurez mediante análisis de imágenes. Estos lotes serán almacenados en líquido si tienen la calidad adecuada y enviados a la industria para mesa o, en seco, si son destinados a la extracción de aceite. En el trabajo se expone el desarrollo de este remolque y su evaluación en condiciones de campo. Por último, también se indica la metodología seguida para digitalizar todo el proceso de cosecha y postcosecha llevado a cabo, registrando en la nube lotes de fruto con la trazabilidad asociada, que contiene tanto las operaciones anteriores a la recolección como las posteriores hasta la entrega en la industria, así como la propuesta de estructura de la información en la nube para continuar dentro de esta hasta llegar al consumidor. Las tecnologías y gestión de la información necesarias son expuestas junto a los resultados de su puesta en funcionamiento. Se ha establecido un patrón característico de molestado atribuible a cada medio de recolección, así como la mejora de la calidad del fruto mediante medidas correctoras en cuanto a la recepción del fruto con superficies acolchadas obteniéndose que el sacudidor de ramas es el método de recolección más agresivo con recepción sobre fardo. Adicionalmente, se puede estimar el molestado real del fruto evaluando parcialmente el fruto y aplicando un factor de corrección. Por otro lado, se han desarrollado monitores de rendimiento basados en sensores ToF que pueden ser una alternativa a las células de carga ya que ofrecen una buena exactitud y son más estables para la evaluación en dinámico del peso de fruto transportado. Por el contrario, factores como la iluminación ambiental y los colores pueden afectar al cálculo del peso. Además, requieren de una configuración adaptada a la geometría del sistema de almacenamiento del fruto. Finalmente, es posible realizar una limpieza y clasificación del fruto en campo que permita realizar lotes de una calidad determinada para enviarlo directamente desde el campo a las industrias de procesamiento para mesa y extracción de aceite, según la calidad evaluada. Así mismo, toda la trazabilidad asociada a los lotes (anterior y posterior a la recolección) puede centralizarse en una plataforma cloud y gestionarse para digitalizar toda la fase productiva en campo. Para ello es necesaria la adopción de tecnologías como RFID, GNSS, IoT, cloud computing y técnicas de procesamiento de imágene

Assessment of the Accuracy of a Multi-Beam LED Scanner Sensor for Measuring Olive Canopies

MDPI. CC BYCanopy characterization has become important when trying to optimize any kind of agricultural operation in high-growing crops, such as olive. Many sensors and techniques have reported satisfactory results in these approaches and in this work a 2D laser scanner was explored for measuring canopy trees in real-time conditions. The sensor was tested in both laboratory and field conditions to check its accuracy, its cone width, and its ability to characterize olive canopies in situ. The sensor was mounted on a mast and tested in laboratory conditions to check: (i) its accuracy at different measurement distances; (ii) its measurement cone width with different reflectivity targets; and (iii) the influence of the target’s density on its accuracy. The field tests involved both isolated and hedgerow orchards, in which the measurements were taken manually and with the sensor. The canopy volume was estimated with a methodology consisting of revolving or extruding the canopy contour. The sensor showed high accuracy in the laboratory test, except for the measurements performed at 1.0 m distance, with 60 mm error (6%). Otherwise, error remained below 20 mm (1% relative error). The cone width depended on the target reflectivity. The accuracy decreased with the target density

Methodology for Olive Pruning Windrow Assessment Using 3D Time-of-Flight Camera

The management of olive pruning residue has shifted from burning to shredding, laying residues on soil, or harvesting residues for use as a derivative. The objective of this research is to develop, test, and validate a methodology to measure the dimensions, outline, and bulk volume of pruning residue windrows in olive orchards using both a manual and a 3D Time-of-Flight (ToF) camera. Trees were pruned using trunk shaker targeted pruning, from which two different branch sizes were selected to build two separate windrow treatments with the same pruning residue dose. Four windrows were built for each treatment, and four sampling points were selected along each windrow to take measurements using both manual and 3D ToF measurements. Windrow section outline could be defined using a polynomial or a triangular function, although manual measurement required processing with a polynomial function, especially for high windrow volumes. Different branch sizes provided to be significant differences for polynomial function coefficients, while no significant differences were found for windrow width. Bigger branches provided less bulk volume, which implied that these branches formed less porous windrows that smaller ones. Finally, manual and 3D ToF camera measurements were validated, giving an adequate performance for olive pruning residue windrow in-field assessment

Cleaning system, batch sorting and traceability between field-industry in the mechanical harvesting of table olives.

The table olive sector, there is no cleaning and fruit sorting by quality in the field of the harvested olive batches, nor is there an exhaustive recording of traceability. As a result, the batches are stored and shipped dry, dirty and with fruit of different qualities. When they arrive at the industry, they are cleaned and sorted according to their quality, maturity index and size into fruit suitable for green or black processing and unsuitable for olive oil processing. Overall, all fruits are mixed regardless of their origin leading to loss or interruption of traceability, so that there are also inefficiencies in the logistical process. To offer a possible solution, this paper proposes a methodology and presents a prototype that enables cleaning and sorting based on quality at the field level, incorporating a liquid transport system to stop or reduce bruising and an application for recording traceability throughout the production cycle. This prototype has been tested in the laboratory with artificial olives to study the sorting algorithm and, subsequently, in field conditions in a harvesting campaign with real fruit. The algorithm reported a mean relative error of 9.02 ± 6.66%, 11.63 ± 9.61% and 10.31 ± 8.85% in the test with 3 predefined sizes and 3 different ripening stages evaluated. In the evaluation test of the fruit bruising with 2% and 10% of controlled damage, results of 2.67 ± 1.74% and 10.09 ± 4.55% respectively were obtained. In the field, the grading machine for small size olives removed 98% of small diameter fruit and the cleaning system worked efficiently. The percentage of correct sorting based on the maturity index reported 89.3% for ’A’ or suitable quality and 76.7% for ’B’ or unsuitable quality. The fruit bruising sorting reported acceptable results influenced by the randomness in the bruising and its positioning with respect to the artificial vision system. The application recorded the previous operations, the batches generated and their characteristics as well as transport to the industry with its associated variables. The methodology and prototype developed may represent an advance in the management of the quality and traceability of table olives from the field level, making the logistics function more efficient and supporting the industry

Machine to machine connections for integral management of the olive production

Most of the advances made in olive traceability are focusing on the arrival of the fruit to industry but it is necessary to manage all the operations involved in the production chain. This work introduces a new machineto- machine system that allows integrating all the information generated from the field to the market through a methodology for the real-time management of all operations carried out. The consumer can check the product history, farmers can consult and optimise their resources, and industry can control the operations performed. The system developed is composed of an electronic device, iOlivetrack-D, mounted on agricultural machinery which identify plots and sends the information generated to a web application, iOlivetrack-W. The system was tested in real working conditions obtaining good results in the identification of plots using RFID and GPS although the limitation about this technology must be considered for a commercial purpose. The registration of information associated in the web application has been carried successfully by an adaptable system that allow the access to the data in configurable ways. To complete the product chain information, industrial processing operations were simulated by entering the data manually through the application. Finally, a QR code was generated to provide consumer access to product traceability information that may be consulted in this paper. The work shows the pros and cons of this system which allows assured traceability of the entire production chain and the proper manage of the production

A smart system for the automatic evaluation of green olives visual quality in the field

Monitoring some of the parameters that affect the quality of table olives for green processing is fundamental in a farmer's decision making. This work develops an affordable system for in-the-field evaluation of fruit calibre, ripeness and bruise index. The system consists of an illuminated cube that acquires images of fruit samples and generates an instantaneous report, using computer vision techniques implemented in software. To do this, it was necessary to determine models of fruit weight and size and also the colour regions (RGB colour space) involved in olive maturity indexes. Moreover, supervised training models were created to perform image segmentation (background and bruising areas). Error in the estimation of fruit weight was very low (R2 = 0.9), and prediction of the maturity index (MI) was quite good, with an accuracy of 0.66 and 0.91 for manually sorted olives in MI0 and MI1 respectively (green processing). Prediction of MI2 had lower precision (0.48) when the fruit was changing to black-purple and the bruising spots were confused with fruit area because of determined similarities in colour. The error in the estimated bruise index was lower for MI0 (RMSE = 2.42) than for MI1 (RMSE = 3.78), both of which are suitable for an estimation of quality in the field. Overall, the system's performance reveals promising results for a quick, easy and accurate evaluation of the external parameters that define the quality of olives. The models obtained could be useful for other purposes

Bruising pattern of table olives (‘Manzanilla’ and ‘Hojiblanca’ cultivars) caused by hand-held machine harvesting methods

This work presents a characterisation of the fruit and the bruising caused by some common detachment methods (manual, stick, shaker comb, branch shaker) and interception methods (net or padding) in common table olive varieties. We took pictures of fruit samples inside a special device, and the images were processed to extract characteristic parameters of shape and size (number of spots, Feret diameter, circularity, colours …). Moreover, we studied the time evolution of bruising caused on the fruit by a controlled impact. Finally, we developed a system that allows synchronised rotation of the fruit with image capture to evaluate bruising on the whole volume of the fruit. Our results showed that different harvesting treatments produced differences in the average number and diameter of spots per fruit, as well as in the average area of the spots per fruit for the different varieties. Fruit colour or bruising can also serve as a control factor for computer vision characterisation, for which reason we recorded differences in the firmness of the bruised and non-bruised areas of fruit. The harvesting method that caused the highest median values of bruise index was the shaker comb, particularly for ‘Manzanilla’ with an index of 1.59% on padding compared to 0.24% for ‘Hojiblanca’. Net interception was also observed to increase the bruise index in ‘Manzanilla’ (5.85%). Bruising assessment that only considers a single photograph means that a considerable amount of bruising remains disregarded compared to the actual bruising on the whole volume of the fruit

An automatic trunk-detection system for intensive olive harvesting with trunk shaker.

Trunk shakers are widely used for olive harvesting, being the main detachment system for fruit harvesting. In recent decades, the components of trunk shakers have evolved at mechanical, hydraulic and control levels. However, machine accuracy depends on the operator, whose expertise is a key factor for issues such as trunk debarking caused by grabbing systems, shaking parameters, or on-foot operator safety. The objective of this work was to develop an automatic trunk-detection system to reduce operator influence on the process. Thus, the automatic system via infrared sensor was implemented on a trunk shaker head hitched to a tractor. A control algorithm, control logic and display for trunk grabbing automation were developed. The automatic system was tested under laboratory and field conditions to assess the influence of some variables on trunk detection. The evaluated variables were colour, material, diameter, and target location within the sensor field of vision. The success rate of the automatic system was 91% for trunk grabbing. In the field phase, the efficacy of the automatic system was compared to an operator performing the tasks manually, obtaining times of 16.05 ± 2.8 s/tree and 21.54 ± 5.29 s/tree respectively, and a percentage of success in trunk grabbing of 92.9%. Automatic mode improved manual mode by saving 27.3% of time, improving effective field capacity. The automatic mode developed here provided a high ratio of success and it showed highly reliable and efficient performance compared with manual mode