Optimizing planning decisions in the fruit supply chain

La agro-indústria xilena ha experimentat un augment constant d'exportacions de fruita processada en la darrera dècada, arribant a un augment del 107% en volum total i 185% en valor. Aquest creixement significa que la cadena de subministrament de fruita fresca, ja sigui per a conserva, deshidratats, congelat i fresc de fruites o sucs, requereix suport per fer gestió cada vegada més eficient. Fins ara, alguns problemes relacionades directament amb la necessitat de millorar la competitivitat del sector no han estat encara tractats. En els últims anys els costos de producció han augmentat degut principalment a l'escassetat de mà d'obra i la mala qualitat de la fruita fresca. Això fa que millorar l'eficiència de cadena de subministrament i per tant la competitivitat agroindústria, requereixi de noves eines que serveixin de suport a la presa de decisions a la cadena de subministrament de fruita fresca. En aquest context, l'objectiu general d'aquesta recerca era desenvolupar un conjunt d'eines per donar suport a les decisions tàctiques de la cadena de subministrament de la fruita i millorar la gestió de compres, de les cambres frigorífiques i el transport. Tres importants contribucions es fan en aquest treball de recerca. La primera d'elles té a veure amb l'estat de l'art de les cadenes de subministrament, revisant els models d'optimització aplicats a les cadenes de subministrament de fruita fresca. La segona consisteix a proporcionar quatre eines per recolzar les decisions tàctiques de les cadenes de subministrament de fruita fresca, en concret, tres models matemàtics per a l'optimització de les decisions que donen suport a la selecció de productors i la compra de fruita fresca, el seu posterior emmagatzematge i transport i la proposta d'un model per a la gestió de cambres frigorífiques. Una tercera aportació és la proposta d'un sistema de suport a la presa de decisió (DSS), permeti la transparència del coneixement dels models anteriors al sector per al seu us pràctic. Destacar el valor addicional del treball a l'haver aplicat els models a casos reals. Així, tots el models proposats van ser validats amb l'ajut de l'agroindústria de la regió centre-sud de Xile que tenien problemes amb la seva cadena de subministrament.The Chilean agro-industry has experienced a steady increase of industrialized fruit exports over the last decade, reaching a total volume increase of 107% and 185% in value. This growth means that the fresh fruit supply chain, either for preserved, frozen, dehydrated, fresh fruit or juices, requires support in order to make management increasingly more efficient. So far, some problems directly related to the need to improve the sector´s competitiveness have not yet been addressed. In the last few years production costs have risen mainly due to labor shortage and poor quality of raw material (fresh fruit). That is why, improving the supply chain efficiency and thus the agro-industry competitiveness, particularly in the center-south region of the country, requires new tools that could support decisions making regarding the fresh fruit supply chain. Within this context, the general objective of this research was to develop a set of tools aiming to support tactical decisions that could enhance management of fresh fruit purchasing, cold storage, transport, and opening of cold chambers. Three important contributions are made in this research study. The first one has to do with the state-ofthe- art of supply chains management, by reviewing optimization models applied to fresh fruit supply chains. The second one consists in providing four tools to support tactical decisions regarding fresh fruit supply chains, specifically, three mathematical models for the optimization of decisions that support the selection of growers and the purchasing of fresh fruit, their subsequent storage and transportation, and the proposal of a mathematical model for cold storage management. The third contribution is the proposal of a Decision Support System (DSS), which aids in decisions about growers selection and purchasing of fresh fruit, as well as its subsequent storage and transportation. Finally, there is an important additional contribution that involves the application of the models to real cases. All models proposed were created and validated with the support of agro-industries from the centersouth region of the country having problems with their supply chain, which were addressed in this research study

An economic analysis of a robotic harvest technology in New Zealand fresh apple industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, Massey University School of Agriculture and Environment, Manawatu, New Zealand

The New Zealand apple industry is predominately an export-oriented industry relying on manual labour throughout the year. In recent years, however, labour shortages for harvesting have been jeopardising its competitiveness and profitability. Temporary immigration labour programs, such as the Recognised Seasonal Employer (RSE) program have not been able to solve the labour shortages, urging the industry to consider use of harvesting automation, i.e. robotic technology, as a solution. Harvesting robots are still in commercial trial stage and no studies have assessed the economic feasibility of such technology. The present study for the first time develops a bio-economic model to analyse the investment decision for adopting harvesting robots compared to available alternatives, i.e. platform and manual harvesting systems, using net present value (NPV) as the method of analysis; for newly established single-, bi-, and multi-varietal orchards across different orchard sizes, and three apple varieties (Envy, Jazz, and Royal Gala); and implications of orchard canopy transition and associated sensitivities are considered. The results of the analysis identified fruit value and yield as the key drivers for the adoption of harvesting automation. For relatively low value and or yielding varieties such as Jazz or Royal Gala, robots are less profitable in single-varietal orchard compared to bi-varietal orchard planted with relatively low value and yielding varieties. In a multi-varietal orchard, a relatively high value and high yield variety, such as Envy, is crucial to compensate for the costs incurred for harvesting other varieties using robots or platforms. The greatest potential benefit of utilising harvesting robots was reducing pickers required by an average of 54% for Envy and 48% for each of Jazz and Royal Gala across all orchard sizes compared to manual harvesting; and 7% in average for each of Envy, Jazz, and Royal Gala across all orchard sizes compared to platform harvesting system. This study also identified the break-even price for a robotic harvester in a single-varietal orchard, showed that the break-even prices exceeded the assumed price of the robot, and are highly variable depending on the varietal value and yield, where Envy as a relatively higher value and yielding variety returns a break-even price of $2.92 million compared to relatively lower value and yielding varieties, Jazz with$674,895, and Royal Gala with $689,608. Sensitivity analyses showed that both harvesting speed and efficiency are key parameters in the modelled orchard and positively affected the net returns of the investment and must be considered by researchers and manufacturers. However, for developers and potential adopters of robots, it should be more important that robots operate faster, but not necessarily as more efficient in order to generate a high return while substituting the highest number of pickers and leaving less unharvested fruit on trees in the limited harvesting window. Reducing robot price by 12% and 42% can generate an equivalent level of profit similar to manual or platform harvesting, respectively. Increases in labour wages, and decreases in labour availability and efficiency adversely affected the NPV and profitability outlook of the investment, but NPV was more affected by the decreases in labour efficiency and availability than wage increases. This research has important science and policy implications for policy makers, academics, growers, engineers, and manufacturers. From an economic perspective, for late adopters or those growers who may not be financially able to invest in robots or may be uncertain about their performance, platform harvesting system can be utilised as an alternative solution that is commercially available until robotic harvesting technology improves or becomes more affordable, and commercially available. Alternatively, it may be possible for these orchardists to benefit from utilising the robotic harvester in the form of a co-operative or contract-harvesting business model to avoid the capital costs associated with purchasing and operating the robots. Besides the economic factors, robotic harvesters have the potential to be considered as a solution for non-economic factors such as food safety problems. This is more apparent in the post-Covid-19 pandemic era, which has not only made it more difficult for growers to source their required workers due to border closures, but also has led consumers to be more cautious about food safety when they make purchase decisions and prefer to have their fresh fruit touchless from farm to plate. This may not be a problem for packhouses as most are automated, but it may be an issue for harvesting operations, because pickers have to pick apples by hand. Even though robots cannot be the only option for growers to rely on for the foreseeable future as they are not commercially available, in the current situation robot harvesting may be the most ideal solution

Determining factors driving sustainable performance through the application of lean management practices in horticultural primary production

Erasmus Mundus Inspire Projec

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto. Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo. Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive. I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati. L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati. I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo. Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta

Climate change impacts on winter chill in Mediterranean temperate fruit orchards

Temperate trees require low temperatures during winter and subsequent warm conditions in early spring to flower and eventually bear fruit. Many parts of the Mediterranean region feature winters with low and sometimes marginal chill accumulation. To assess historic and future agroclimatic conditions for cultivating temperate trees (including almonds, pistachios, apricots, sweet cherries and apples), we mapped winter chill throughout this important growing region. We used on-site weather records (1974–2020) to calibrate a weather generator and produced data for historic and future scenarios. To broaden our analysis, we spatially interpolated chill for the whole Mediterranean basin. We supplemented our simulation outcomes by collecting expert knowledge (from farmers and researchers) regarding observed climate change impacts on temperate orchards as well as future risks and concerns generated by climate change. Results showed that northern African growing regions have experienced major chill losses, a likely cause of the irregular and delayed bloom highlighted by experts. The same regions, together with southern Europe, may lose up to 30 Chill Portions by 2050 under a moderate warming scenario. For the future, experts foresee increasing risk of spring frost in early-blooming cultivars, exacerbated bloom-related problems and increasing occurrence of heat waves. Our results provide evidence of likely climate change impacts on temperate orchards. Expert knowledge proved instrumental in interpreting the simulation results as well as in orienting climate change adaptation strategies. The results we present are useful for farmers and orchard managers planning new plantings, as well as for researchers and policy makers developing strategies to adapt fruit orchards to the impacts of climate change.Publishe

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings