The way avocados are handled in the packhouse has a direct impact on their shelf life and the state in which they arrive in the consumer’s kitchen. Technology now exists to quantify packhouse performance and to pinpoint areas to improve fruit quality.
Sadly, 2019 was not an exceptional season for the local avocado industry. It lost R8.4 million in that year when, due to poor product quality, more than a million kilograms of fruit was processed to oil and guacamole, instead of being sold as fresh fruit. Indeed, quality losses have cost the avocado industry vast amounts of money every year. Fortunately, new technology developments can solve the quality headache.
Smart technology breakthroughs
Agriculture and civil engineering are not usually regarded as partners, but the University of Pretoria is proving the contrary. A collaboration between the Faculty of Engineering, Built Environment and Information Technology; and the Faculty of Natural and Agricultural Sciences resulted in data-logging devices that enable a better understanding of the farm-to-table journey of avocados.
Having successfully developed the data-logging technology, the university, the Postharvest Innovation Project and the South African Avocado Growers Association (SAAGA) joined forces to advance its application. The goal was to determine the potential of packhouse operations (and possible packhouse optimisation) to reduce postharvest losses, along with lowering the postharvest stress associated with mechanised packlines and high-volume processing.
The research project was registered by Prof. Wynand Steyn, Head of the Department of Civil Engineering, and Prof. Lise Korsten, Co-Director of the DST-NRF Centre of Excellence in Food Security, University of Pretoria. The Post-Harvest Innovation (PHI) Programme and SAAGA each contributed R232 043 towards the project.
The study was managed by André Broekman, who is currently doing his PhD in railway engineering, as an auxiliary project. He was supported by a team of principal investigators consisting of Dr Malick Bill, Sarel Coetzer, Dave Ventura and Abrie Cilliers.
Six smAvo devices were placed in 24 packhouses across Limpopo, Mpumalanga and KwaZulu-Natal during the 2021 harvesting season, to obtain around 139 datasets. The participating packhouses varied significantly in terms of age, capacity and degree of mechanisation.
More than 12 million acceleration measurements were recorded from the smAvo loggers, providing data on (among others) the time avocados spend on the packline, the bumps and falls they experience (impact forces) and handling conditions.Read More
Six smAvo devices were placed in 24 packhouses across Limpopo, Mpumalanga and KwaZulu-Natal during the 2021 harvesting season.
Processing the more than 12 million data points was a mammoth task that prompted the research team to develop new software to get the job done. Their efforts resulted in the packhouse processing quadrant (PPQ), a system that classifies packhouses according to a damage index score (DIS). The DIS ranges from one (an optimised packhouse) to four (an unoptimised packhouse).
The team furthermore developed a simplified neural network to predict a packhouse’s classification based on nine statistics that are recorded by the smAvo devices. The calibrated model that resulted from the neural network can predict the DIS of a given packhouse with 82% accuracy, outperforming the best linear model.
This calibrated model provides the starting point for accurate, integrated packhouse instrumentation, while simplifying the analytical processes associated with large datasets.
The DIS of a packhouse is an indication of where a facility falls on the spectrum of optimisation, compared to other packhouses.
Mechanical rollers were one of the most detrimental components in the packline, causing the most severe and longest lasting impacts. The time that the smAvo devices spent being “roughed up” by mechanical rollers varied substantially within a given packhouse, mainly due to the uneven volume of avocados moving through the packline. In general, packlines with slower, smaller rollers delivered a lower DIS score.
According to the DIS, bins are slightly kinder to avocados than crates, although this is likely a by-product of less sophisticated packhouses that process smaller volumes of avocados.
Not surprisingly, staff training emerged as being critical for the better treatment and handling of avocados in packhouses.
The DIS of a packhouse is not inherently good or bad; it is, instead, an indication of where a facility falls on the spectrum of optimisation compared to other packhouses.
What the future holds
The next generation of smAvo instrumentation should be able to classify packhouse performance in real time and relay the information to a cloud-based digital twin of the facility.
This digital twin can be designed to adjust the number of avocados in the packline, and change directed chemical sprays and the speed of the packline to reduce the postharvest stress. With digital twinning, packhouse operations can be adjusted immediately and even in anticipation of the avocados that are about to arrive. It is a vast improvement on the reactive interventions that are currently the only option.
Low-power, long range (LoRa) communications technologies, combined with edge intelligence, are becoming more and more common in agricultural Internet-of-Things (IoT) networks. The smaller these technologies become, the more innovatively they can be used in the fresh produce sector.
Research is currently underway to deploy such technologies to monitor road and sea transportation routes to comprehensively map the farm-to-fork journey of fresh produce. These data-driven investigations promise not only insights for producers, but will also guide road authorities in their quest to improve infrastructure quality to the benefit of the larger community.
Photography by ANDRÉ BROEKMAN