Optimizing Cannabis Post-harvest Operations
PickApp's advanced system focuses on collecting information to boost efficiency and increase productivity
just as in any modern industrial manufacturing line, key operational parameters like productivity and quality must be constantly monitored and improved when growing crops. pickapp is an advanced system that does just that, because it was designed for farmers—by farmers. its focus is on collecting information, and, as far as cannabis, it serves three main purposes:
• streamlining the workforce – detailed information on employee productivity makes it possible to set standards that lead to higher cost efficiency
• quality control – collecting relevant data helps ensure products meet the desired levels of quality and uniformity at all times
• research and learning – data analyzed properly contributes to building knowledge
allocating resources just right – the ein hatzeva story
statistically, in cannabis greenhouses and in intensive farming, labor accounts for about 40% of the costs of the growing process. when it comes to post-harvest processing, we have no data.
as ilan chanochi, customer success manager of pickapp told us, “ein hatzeva, a large commercial medical cannabis farm in israel’s arava desert, entrusted us with the task of helping them improve cultivation.
in light of our success in boosting efficiency and reducing costs, maya selvin markbright, one of the farm’s owners and director of post harvest, asked us to continue down the line, and implement pickapp in the post-harvest trimming process too. sure enough, two months later we received amazing feedback: they saw a 40% increase in productivity, along with a significantly enhanced final product. quality issues may originate in any of three stages of production: growing, processing and drying, and trimming. by implementing pickapp in trimming, she was able to monitor each worker and give them precise feedback, help them curb the number of defects, and refine their skills at the same time. the system gave her the tools she needed to decide which strains were more economically viable and worth keeping. even when managers are already aware of a situation, here they are shown data in a way that allows them to hone in on specific results they wish to achieve.”
based on this case, the pickapp team reached some important conclusions about the system’s effectiveness in post-harvest ops:
• both farm workers and managers were accustomed to a different production standard, which was random in a way, and based mainly on what they had “heard” rather than on accurate statistical data.
• since the final product is not homogeneous, growers must be able to trace the origin of any problem to the pre-harvest or early post-harvest stages (drying). for instance, mildew on the flower may have developed pre-harvest, or through a flaw in the drying process.
it is important to note that although cannabis projects require major investments, post-harvest is still managed using excel spreadsheets filled manually or by the software that only collects info for regulation purposes. one would expect this emerging field, that in general is run using advanced technology, to enjoy greater precision at every stage. in practice, there is still much room for modernization.
each farm has its own varieties and develops its own methods, as there are no established standards or protocols yet. we can now change all this. pickapp eliminates manual data entry because, by simple scanning, it immediately collects all the data from the area, including photographs, which can be uploaded to the system for documentation. agronomists looking at the photographs can easily identify the cause of a quality issue—fungi, excess moisture, and the like.
pickapp allows growers to plan in advance. based on data received, managers can change the instructions for future growing work, and even do so online, i.e. asking a worker to move from one row to another.
returns here are significant thanks to optimization in workforce allocation and improved class a product quality, and all this within a relatively short payback period. once the number of labor hours required per row is calculated, realistic average times can be set. from here, a standard can be derived for post-harvest too, minimizing slack or bottlenecks. rather than dictate, the system provides farmers with data and tools for determining workloads that maximize the efficiency of the workforce, the main component of a farm’s costs.
quality is another area into which the pickapp system has ventured – both for breeders and growers, who have seen dramatic increases in revenues. proper quality data collection helps managers develop specific protocols for each strain, and make better business decisions about each variety regarding feasibility, hours, labor, etc. one of our customers is a seed company that conducts research and trials for the development of new avocado varieties. instead of recording data manually, the information is quickly scanned right into the system, making it easier for researchers to compute, analyze the data, and reach conclusions.
pickapp also facilitates control of multiple operations, helping managers compare the performance of several farming units simultaneously.
sounds complex? not at all. as opposed to other systems available in the market, pickapp does not require large investments for the purchase of equipment. it can be installed on any android smartphone!
we invite you to contact us at email@example.com and set up a demo meeting.
this article was published in ‘medical cannabis 2022’