Agricultural Drone Pest Detection Patents: Leaders & White Space 2026
A patent landscape review of agricultural drone pest detection: filing trends since 2015, the IPC mix across A01M and B64C, concentration among the ranked leaders, and where claim space remains open.
Top-5 share = the 5 largest assignees ÷ all 16 records in scope (CR5), not the ranked leaders only.
What this landscape covers
This review covers patent filings at the intersection of agricultural drones and pest detection, drawing on 16 patent families published between 2015 and the 2026 data cut-off. The scope is defined by IPC classes A01, A01M and G06V combined with text and title/abstract/claims searches for drone-based pest detection, so it captures systems that pair an aerial platform with pest or insect recognition rather than agricultural drones in general.
Filing activity is thin and recent: the earliest year in scope shows no records, and the field only builds volume from the early 2020s onward. Publication lags filing by roughly 18 months, so the most recent year shown will always understate real activity — treat the last one or two years as a floor, not a ceiling.
Filing trend and technology composition
Sixteen records is a small enough set that a single year or a single filer can swing the shape of the chart. Read the trend and the IPC split together: they show a field still forming its own classification identity rather than settling into an established sub-category.
A short filing history, peaking in 2023
The trend runs from zero records in 2017 to a peak of 6 in 2023, with 2026 (a partial year) already showing 2 filings. There are fewer than four complete years of data once publication lag is accounted for, so no growth rate can be stated responsibly — the honest read is that filings are recent and concentrated around 2022-2023 rather than steadily rising.
Pest control and aircraft classes dominate
A01M (pest and vermin control) appears on 81.3% of the 16 records in scope and B64C (aeroplanes and helicopters) on 62.5%, together confirming that most filings genuinely combine a flying platform with pest-control functionality rather than detection alone. Computing and AI-related classes — G05D, G06N, G06V — each sit well below that, at 31.3%, 25.0% and 18.8% respectively, meaning the analytics layer is present but not yet the primary claim focus for most filers.
Shares are the percentage of the 16 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
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Try EurekaA representative filing in the field
Drone-based spectral imaging and artificial intelligence for early detection of plant diseases
Manual monitoring of crop health is labor-intensive, time-consuming, and often inaccurate, leading to delayed responses, excessive pesticide use, and yield loss. This project presents an AI-powered autonomous drone system designed to revolutionize precision agriculture. Equipped with NDVI and multispectral cameras, the drone captures high-resolution spectral data to identify early signs of plant stress, pest infestations, nutrient deficiencies, and water imbalance. The onboard AI model processes this data in real time, geotags affected zones, and autonomously performs precision spraying through an intelligent dispensing mechanism, ensuring optimal chemical use while minimizing environmental impact.Filed by Sri Sairam Engineering College, published 2026-01-09.
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A handful of filers set the field's direction
The top 5 ranked assignees combined account for 56.3% of all 16 records in scope, and the top 10 extend that to 87.5%. With a field this small, that means most of the remaining ranked entrants — of 49 total — hold only one filing each, so competitive attention should focus on the leading names rather than a broad watch list.
Filing activity is heavily India-weighted
Ten of the 16 records in scope were filed through the Indian receiving office, with the remainder split thinly across Europe, Spain, Hong Kong, Poland and Turkey. That skew suggests the immediate commercial and regulatory pressure for drone-based pest detection is being tested first in Indian agriculture, with only isolated filings elsewhere so far.
Platform and payload claims outweigh pure AI claims
A01M and B64C together dominate the classification mix, at 81.3% and 62.5% of the 16 records respectively, while AI-specific classes such as G06N sit at 25.0%. That gap indicates most granted claim language still centres on the physical platform and pest-control mechanism, leaving the detection-algorithm layer comparatively less staked out.
Co-filing exists but stays shallow
Ten co-assignee pairs appear across the dataset, with the strongest links running through a single research organisation and academic partners. None of these pairings recur more than once, so co-filing in this field looks exploratory rather than the product of an established consortium.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to agricultural drone pest detection patent landscape, with the prior art for and against each one.
Where to look next
The dataset points to a field still small enough to map by hand, but the questions it raises are the ones worth taking further with dedicated search and claim-mapping tools.
Map the detection-algorithm layer separately
G06N and G06V classes sit well below A01M and B64C in this dataset, suggesting the AI detection layer is under-classified relative to the platform. A targeted search on that layer alone may surface adjacent filings this scope excludes.
Explore in Patsnap EurekaTrack the Indian filing cluster
With 10 of 16 records filed via the Indian receiving office, monitoring new Indian filings is the fastest way to catch the next entrant before it reaches other jurisdictions.
Set up a watch in Patsnap EurekaRevisit after the 2026 filing year closes
2026 is a partial year with only 2 records so far, and publication lag means the true 2025-2026 filing volume is still understated. A follow-up review once the lag clears will give a fairer read of the current trend.
Run a fresh landscape in Patsnap EurekaQuestions practitioners ask about this field
Within the scope defined for this review — IPC classes A01, A01M and G06V combined with drone and pest-detection search terms — there are 16 patent families published between 2015 and the 2026 data cut-off. This is a narrow, precisely defined slice rather than a count of every agricultural drone patent, so broader searches using different classification codes will return different totals. Treat 16 as the size of a specific, tightly scoped field rather than the whole of agricultural drone technology.
The ranking covers 49 assignees, and it is genuinely the full ranked list the dataset returns, not a top-50 or top-100 cut. A small number of leaders account for a majority of filings — the top 5 combined hold 56.3% of all 16 records, and the top 10 hold 87.5% — while most of the remaining ranked entrants have filed only once. That pattern means competitive monitoring should prioritise the leading few names rather than trying to track the entire ranked list equally.
Most filings combine a pest-and-vermin-control classification (A01M, present in 81.3% of the 16 records) with an aircraft classification (B64C, present in 62.5%), meaning the bulk of the field is about pairing a flying platform with pest-control functionality, not just imaging or detection software. Classes tied to computing and AI models, such as G06N and G06V, appear in a smaller share of records — around a quarter or less — indicating the analytics layer is present but less heavily claimed than the physical platform and control mechanism. A record can carry more than one class, so these shares overlap rather than sum to 100%.
The filing trend runs from zero records in 2017 to a peak of 6 in 2023, with the 2026 year (partial, as the data cut-off falls mid-year) already showing 2 records. There are fewer than four complete years of data once the typical 18-month publication lag is accounted for, so no reliable growth rate can be stated from this dataset. What can be said is that filing activity is recent and clustered around 2022-2023 rather than spread evenly across the review period.
The classification data shows AI and image-recognition classes (G06N at 25.0%, G06V at 18.8%) trailing well behind the platform and pest-control classes that dominate the field, which suggests the detection-algorithm layer is comparatively under-claimed relative to the aerial platform itself. Business-process classes like G06Q, covering data services built on top of detection, sit at a similarly modest 18.8%. Geographically, filings outside India are thin — only single-digit counts across Europe, Spain, Hong Kong, Poland and Turkey — pointing to open ground in jurisdictions beyond the current India-heavy cluster.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.