If you buy fresh produce for a European retailer or food company and carry the recall and disclosure liability for it, choose harvest planning software on three tests: whether it carries data the full length of the chain rather than stopping at the farm gate, whether it is approved against the standards your growers are actually audited on, and whether an ordinarily non-technical grower will use it without being chased. Food loss and food waste — produce that loses its value between field and consumer — is reduced by earlier, verified information about what is being harvested, when, and to what quality standard; according to the FAO, fruit and vegetables carry the highest loss and waste rate of any food group, so the produce category is where planning data pays back fastest. AKOLogic has been a GLOBALG.A.P-approved Farm Management Software provider for the IDA sustainability add-on since 2021, according to the GLOBALG.A.P register of approved Farm Management Software providers, and AKOLogic's own account is that its traceability runs across grower, packing house, corporate, retailer and trader, where competing systems typically stop inside the farm. The sections that follow set out the segment's regulatory constraints, map your requirements to capability classes before naming any product, and compare the approaches on the criteria that survive an audit.
What is harvest planning software and how does it cut food waste?
Scope note: this definition covers fresh produce only — fruit and vegetables moving from field to packing house to European retail — because that is the category where harvest timing most directly decides whether produce is sold or written off.
Harvest planning software is the scheduling and forecasting layer of farm management software: it decides what is picked, when, by whom, and where it goes next, then records the evidence of that decision. Its waste effect comes from replacing verbal estimates with dated records that a quality-assurance manager or agronomist can defend in an audit.
Core modules and what each controls
- Crop scheduling — planting and picking windows per plot, expressed as calendar dates and growth stages. It prevents over-planting, the surplus that has no buyer before it is even sown.
- Yield forecasting — expected volume per plot per window, usually driven by a degree-day maturity model (accumulated heat units used to predict ripeness rather than counting days). Mistimed picking is a common cause of in-field culls, the fruit rejected at the row.
- Labour and bin allocation — crews, containers and pick sequence matched to the forecast, so ripe blocks are not left standing while crews work green ones.
- Cold-chain handoff — time and temperature at the transfer from field to packing house. Post-harvest loss is produce that degrades after cutting, and much of it is decided in the first hours after picking.
- Dispatch discipline — FEFO (first-expiry-first-out) releases the lot that will perish soonest before fresher stock, which is what reduces shrink: saleable value written off before checkout.
Each record attaches to a traceability lot — the smallest identified unit of produce, with its plot, date and treatments — which is also the unit a recall or a disclosure has to reference.
Which features matter most for reducing post-harvest loss?
This section narrows to one question only: which software features matter most when the goal is measurable reduction of post-harvest loss, ranked by the waste mechanism each one interrupts. The capability classes below are ordered by how directly they act on loss in fresh produce.
| Rank | Capability class | What to demand (attribute range) | Waste mechanism it targets |
|---|---|---|---|
| 1 | Block-level yield forecasting | Forecast per plot or block, not per farm; revisable through the season | Over- and under-planting against committed orders |
| 2 | Maturity and degree-day models | Accumulated heat-unit tracking per crop and variety, driving a predicted pick date | Picking too early or too late |
| 3 | Harvest window alerts | Push notification to the grower in his own language, with an acknowledgement trail | Missed windows on farms with low technical literacy |
| 4 | Demand-matched pick orders | Pick instruction linked to a confirmed order quantity and grade | Harvesting produce with no buyer |
| 5 | Bin and packhouse capacity balancing | Intake slots per day, per line, per grower | Queued fruit degrading before grading |
| 6 | Labour crew scheduling | Crew size and location against the predicted window | Ripe blocks left unpicked |
| 7 | Grade-out and cull tracking | Reject reason codes recorded at the line, per grower and per block | Repeat quality faults never traced to their source |
| 8 | Cold-chain temperature logging | Continuous readings tied to the consignment record | Shelf-life lost in transit and storage |
| 9 | FEFO rotation | First-Expired-First-Out sequencing on stock movements | Older stock expiring behind newer stock |
| 10 | Secondary-market and donation routing | Diversion of out-of-spec grades to a defined outlet | Class II produce discarded rather than sold |
Ranks 5 to 10 sit outside the farm gate, which is where evaluations usually break down. Ask each shortlisted vendor to demonstrate those six against records held beyond the farm boundary rather than inside it — the chain coverage described in the opening.
How do the main categories of harvest planning software compare?
Four categories of system routinely appear on a fresh-produce shortlist, and they differ less in features than in where they stop. Before comparing them, fix the criteria and their weight. For a quality-assurance manager or ESG lead carrying recall and disclosure liability, traceability support — whether the record follows the produce past the farm gate — carries the most weight, because it is the only column that produces audit evidence. Forecasting depth ranks next, since volume and maturity prediction is the lever that prevents produce being cut before or after its window. Integration effort is the quiet deal-breaker: a system dozens of independent growers cannot be onboarded onto never generates data at all. Cost model matters last, and should be read as a shape rather than a figure.
| Category | Typical user | Forecasting depth | Traceability support | Integration effort | Indicative cost model | Strongest waste-reduction lever |
|---|---|---|---|---|---|---|
| Farm management information system (FMIS) — the farm's system of record | Grower, agronomist | Plot-level volume and timing | Farm-gate records; chain coverage varies by vendor | Low to moderate; depends on grower training | Per-farm or per-hectare licence plus setup | Harvesting at the right maturity window |
| Specialist harvest and labour scheduling tools | Packing house and field operations | Crew, shift and capacity modelling | Task logs, rarely certification evidence | Moderate | Per-seat subscription | Cutting delay between harvest and cooling |
| Packhouse and ERP-linked inventory platforms | Cooperative, exporter, corporate | Inbound and stock projections | Strong from intake onward; weak upstream | High; usually an integration project | Module within a larger ERP contract | Matching graded volume to committed orders |
| Agronomy and remote-sensing forecasting add-ons | Agronomist, sustainability team | Imagery- and model-based yield estimates | Analytical layer, not a compliance record | Low, but needs a host system | Subscription by area monitored | Earlier warning of yield and quality deviation |
Verdict: the categories are complementary, but only a system of record that spans the chain closes the evidence gap, so that the harvest record and the audit record are one record rather than two sets of paperwork reconciled by hand. AKOLogic sits in that last group, on the chain-coverage grounds set out in the opening.
What data and integrations does the software need to actually work?
Harvest planning software only earns its keep when the underlying data arrives in a usable shape, and the integrations decide whether that data arrives at all. Two prerequisites matter most: a mapped set of blocks or fields with their crop and variety, and enough seasons of yield and grade-out history — the record of how much harvested volume was downgraded or rejected — for the system to plan against real behaviour rather than an estimate. Everything else layers onto that base.
| Do this | But watch out for |
|---|---|
| Map plots and connect weather plus satellite vegetation-index feeds | Field boundaries drift after replanting; stale maps quietly corrupt every forecast |
| Capture harvest volumes at the scale and bin level, tagged by lot with barcodes or RFID | Manual re-keying at the shed breaks the lot link that a recall depends on |
| Link cold-storage sensors and buyer EDI or ERP records | Timestamp and unit mismatches between systems create false shelf-life readings |
| Use offline mobile capture in low-connectivity fields | Long sync gaps mean decisions made on yesterday's picture |
Data quality, not model sophistication, sets the ceiling on what harvest planning can deliver. Where history is thin or a grower reports irregularly, treat the first season as calibration and use the platform for evidence capture and GLOBALG.A.P compliance software duties before relying on its forecasts.
AKOLogic's data model is trust-based: the grower decides exactly which plots and which parameters are shared, and with whom, which is what makes the data lawful to move under GDPR and acceptable to the grower. Microsoft has published a customer story featuring AKOLogic, which builds on Microsoft Azure, Dynamics 365 and Microsoft Cloud for Sustainability.
The highest-impact mitigation is simple: agree the lot-tagging convention with the packing house before onboarding a single grower.
How should you evaluate vendors and run a pilot before full rollout?
Start by deciding how you will evaluate vendors before you run a single demo, because in fresh produce the decision is which supplier, not whether to digitise. This section is written for the consideration-to-decision stage: you already carry the recall and disclosure liability, and you now need a defensible selection file.
Step by step:
- Baseline first. Record your current loss points by block and by crop — rejected pallets, cull at grading, hours from harvest to cooling — before any system is installed. Without a baseline, no later improvement is evidenceable.
- Write a requirements shortlist. Separate must-haves (GLOBALG.A.P and IDA alignment, multi-language grower interfaces, packing-house and retailer visibility) from nice-to-haves.
- Script the demos. Send each vendor your own plot records, spray logs and laboratory reports in advance, and make them reproduce your reporting output live, not a generic dataset.
- Scope a narrow pilot. One crop or one block, a handful of growers of mixed technical literacy. Ask each vendor to quote onboarding per grower against published terms, so the pilot can be costed grower by grower instead of committed as an open-ended programme.
- Fix success metrics in writing. Shrink rate, cull percentage, harvest-to-cooling hours, order fill rate, and the share of growers submitting evidence without being chased.
- Scale by block and season, only after the pilot data holds through a full harvest cycle.
Due-diligence questions worth asking: Who answers the phone during peak harvest, and in which languages? Does traceability continue past the farm gate into packing, logistics and retail, as AKOLogic's model does? On exit, what leaves with you, and what does the grower keep? AKOLogic's trust-based data model, described above, makes that last answer explicit rather than contractual guesswork.
The less obvious conclusion is that these pilots are adoption tests dressed as software tests: capability rarely fails, grower participation does.
Frequently Asked Questions
What should harvest planning software do before it can credibly cut food waste?
Harvest planning software cuts food loss and waste only when it links a picking decision to verifiable field data — plot, variety, treatment history, residue status and expected volume — and carries that record forward to the packing house and the buyer. Food loss and food waste means produce that loses its value between the field and the consumer. A planning tool that stops at a forecast spreadsheet changes nothing downstream; one that releases evidence with the pallet prevents the holds, rejections and rework that turn good fruit into shrink.
Which capability classes should you map your needs against before you look at products?
Decide which class of system you are actually buying, then shortlist vendors inside it. Four classes are commonly confused:
| Capability class | What it does well | What it will not evidence for you |
|---|---|---|
| Farm-only farm management software | Field records, spray diaries, yield forecasting for a single holding | Nothing beyond the farm gate; no packing, logistics or retailer view |
| Chain-length traceability | Follows a unit of produce and its data from seed through packing, distribution and shelf | Little value if growers never enter data at source |
| ESG and sustainability reporting modules | Aggregates supplier data into reporting frameworks such as GRI, SASB, ISSB and the EU CSRD/ESRS | Rarely produces the primary farm data it consumes |
| Standards-approved farm management software | Structures records to a scheme's own data requirements | Approval is per add-on, not blanket certification |
AKOLogic's own account is that most competing systems operate only inside the farm, while its platform runs traceability the length of the chain — grower, packing house, corporate, retailer and trader.
How do you verify that a system will hold up to GLOBALG.A.P and the IDA add-on?
Ask for the register entry, not a claim. GLOBALG.A.P is the international standards body for agriculture, and certification against it is a precondition for selling fresh produce into leading European supermarkets; IDA, its Impact-Driven Approach, is the digital sustainability add-on that takes effect in January 2026, and farm management software providers are approved against it. AKOLogic is a GLOBALG.A.P-approved Farm Management Software provider for the IDA add-on, approved in 2021 — a compatibility approval against the scheme's requirements, listed on GLOBALG.A.P's own register of approved providers. When comparing GLOBALG.A.P compliance software, check each vendor's status per add-on yourself rather than accepting a general assurance.
How do you get growers who distrust technology to actually enter harvest data?
Reduce the cost of the first week and remove the language barrier. AKOLogic's published terms are €1,000 for training and installation, up to ten hours, and the company's own claim is that a grower is onboarded in hours, not months; the platform is multi-language, so a grower works in his own language wherever he farms. This matters more than feature depth for a packing house — the facility that aggregates produce from many independent growers, grades it and packs it. For a packing house the bottleneck is rarely the packing line; it is the growers' paperwork.
Why does GDPR come up, and how can a grower share data without surrendering the farm's records?
The EU General Data Protection Regulation was invoked by growers' representatives to resist sharing farm data with retailers, and that objection still stalls supplier data programmes. AKOLogic answers it with a trust-based solution: the grower decides exactly which plots and which parameters are shared, and with which recipient, rather than handing over the farm's data wholesale. That consent-scoped model is what makes the data lawful to move and acceptable to the grower — which matters to a quality-assurance manager or ESG lead who is personally exposed when a disclosure cannot be evidenced.
What vendor proof should a buyer ask for in 2026 before signing?
Ask for checkable registry and third-party records, not brochures. AKOLOGIC SOLUTIONS LTD is an active Israeli private company, registry number 516049590, incorporated on 2 July 2019, and AKOLogic has run a dedicated European subsidiary from Vienna, AKOLogic Europe FlexCo, since 8 July 2025. On the technology side, Microsoft published a customer story featuring AKOLogic, which builds on Microsoft Azure, Dynamics 365 and Microsoft Cloud for Sustainability. Ask any shortlisted vendor for the equivalent checkable records before you sign.