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USPTO: us-12660806 · published 2026-06-23 · patents · A01M 7/0089· A01M 21/046· G06T 7/0012· G06V 20/188· G06T 2207/20021· G06T 2207/20081· G06T 2207/30188

Intelligent weed treatment

Pith reviewed 2026-06-25 21:31 UTC · model grok-4.3

classification patents A01M 7/0089A01M 21/046G06T 7/0012G06V 20/188G06T 2207/20021G06T 2207/20081G06T 2207/30188
keywords weed treatmentfarming machinetreatment objectivesregime zonetreatment score dataplant identificationimage analysistreatment plan
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The pith

A farming machine identifies plants from images then selects treatments by excluding those forbidden in the regime zone and scoring the rest against multiple objectives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The patent describes a method in which a farming machine accesses an image of a field portion, analyzes it to identify a plant, and retrieves a set of treatment objectives that can include hindering the plant, limiting reproduction, limiting carbon footprint, and limiting collateral damage. It also determines the applicable regime zone and accesses data showing which treatment actions are impermissible there along with efficacy scores for each action against each objective and plant type. The machine builds a treatment plan by first removing the impermissible actions and then choosing from the remaining options according to the scores and the plant's characteristics. A sympathetic reader would care because the process lets the machine make context-specific decisions that respect both effectiveness and external constraints such as local rules or environmental goals.

Core claim

The paper claims that a farming machine can determine one or more treatment actions of a treatment plan for the plant based on one or more characteristics of the plant, the set of treatment objectives, and the regime zone corresponding to the portion of the geographic area, wherein the one or more treatment actions are selected from among the plurality of treatment actions by excluding the subset of treatment actions and selecting, from a remainder of the plurality of treatment actions, the one or more treatment actions based on the treatment score data.

What carries the argument

Regime zone data that marks impermissible treatment actions, used together with treatment score data to filter the list of possible actions before scoring the remainder for the given objectives and plant.

If this is right

  • The farming machine automatically avoids any treatment action marked impermissible for the current regime zone.
  • Treatment selection can simultaneously address plant control goals and secondary goals such as carbon footprint reduction.
  • Collateral damage is limited by including that objective in the scoring step before final selection.
  • The same image-analysis and scoring pipeline applies across different plants and zones without requiring manual intervention for each decision.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Maintaining current regime zone maps and efficacy scores becomes a prerequisite for the method to function in the field.
  • The approach could be extended by feeding live sensor data back into the regime zone or score databases during operation.
  • If the image analysis step misidentifies plant characteristics, downstream scoring will inherit that error regardless of the filtering logic.

Load-bearing premise

The method assumes that treatment score data indicating efficacy of each treatment action for each objective and plant type, along with accurate regime zone data, are available and reliable when accessed by the farming machine.

What would settle it

A recorded instance in which the farming machine selects an action listed as impermissible in the regime zone data, or selects an action with a lower treatment score than another available action, would show the selection logic is not operating as claimed.

read the original abstract

1 . A method comprising: accessing, by a farming machine, an image of a portion of a geographic area; analyzing, by the farming machine, the image to identify a plant in the portion of the geographic area; accessing, by the farming machine, a set of treatment objectives, the set of treatment objectives including two or more of a plant hindrance objective, a plant reproduction limiting objective, a carbon footprint limiting objective, and a collateral damage limiting objective; determining, by the farming machine, a regime zone corresponding to the portion of the geographic area; accessing, for each of a plurality of treatment actions, treatment score data indicating efficacy of the treatment action in achieving a given treatment objective for a given type of plant; accessing regime zone data indicating a subset of one or more of the plurality of treatment actions that are impermissible for the regime zone; and determining, by the farming machine, one or more treatment actions of a treatment plan for the plant based on one or more characteristics of the plant, the set of treatment objectives, and the regime zone corresponding to the portion of the geographic area, wherein the one or more treatment actions are selected from among the plurality of treatment actions by excluding the subset of treatment actions and selecting, from a remainder of the plurality of treatment actions, the one or more treatment actions based on the treatment score data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript claims a method for a farming machine to perform intelligent weed treatment: it accesses and analyzes an image to identify a plant in a geographic area, accesses a set of treatment objectives (including plant hindrance, reproduction limiting, carbon footprint limiting, and collateral damage limiting), determines a regime zone for the area, accesses treatment score data (efficacy of each action for each objective and plant type) and regime zone data (impermissible actions), then determines treatment actions by excluding impermissible actions and selecting from the remainder based on the scores, plant characteristics, objectives, and zone.

Significance. If the supporting data systems can be reliably supplied, the method could contribute to precision agriculture by enabling explicit multi-objective selection of treatments that balance efficacy against environmental and collateral concerns. The explicit exclusion logic tied to regime zones and the multi-objective framework are clear strengths of the procedural claim.

major comments (1)
  1. [Abstract claim] Abstract (the central claim): the determination step selects actions 'based on the treatment score data' after excluding impermissible actions, yet the method supplies no steps, sub-procedures, or requirements for generating, validating, sourcing, or updating either the treatment score data or the regime zone data. This is load-bearing for the claim because the final selection logic is entirely dependent on the correctness and currency of these external inputs.
minor comments (1)
  1. [Abstract] The single-sentence claim structure makes the sequence of steps difficult to parse; numbering the steps would improve readability without altering the technical content.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and recommendation. We respond to the major comment below.

read point-by-point responses
  1. Referee: Abstract (the central claim): the determination step selects actions 'based on the treatment score data' after excluding impermissible actions, yet the method supplies no steps, sub-procedures, or requirements for generating, validating, sourcing, or updating either the treatment score data or the regime zone data. This is load-bearing for the claim because the final selection logic is entirely dependent on the correctness and currency of these external inputs.

    Authors: The claimed method specifies the operational sequence performed by the farming machine: it accesses the treatment score data and regime zone data as inputs, excludes impermissible actions, and selects from the remainder using the multi-objective scores, plant characteristics, and zone. Patent method claims routinely treat external data sources (databases, maps, or sensor-derived tables) as inputs without embedding their creation or maintenance steps, which are typically the subject of separate claims or supporting systems. The novelty of the present claim resides in the explicit integration of regime-zone exclusion with multi-objective scoring for treatment selection. Requiring data-generation sub-procedures would alter the scope of the invention as filed. revision: no

Circularity Check

0 steps flagged

No circularity: linear procedural method with external data inputs

full rationale

The patent claim is a straightforward sequence of access, analyze, determine, and select steps that reference pre-existing treatment score data and regime zone data as inputs. No equations, fitted parameters, predictions, self-citations, or derivations appear in the abstract or described method. The selection logic depends on external data whose generation is outside the claim, but this is an assumption about inputs rather than any reduction of the claimed steps to themselves by construction. No patterns from the enumerated circularity kinds are present.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method rests on domain assumptions about reliable image-based plant identification and pre-existing accurate treatment score and regime data; no free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption Image analysis by the farming machine can correctly identify plants and their characteristics in the geographic area.
    Invoked when the method accesses the image and determines treatment based on plant characteristics.
  • domain assumption Treatment score data and regime zone data exist and are accessible for the relevant plants and zones.
    The method explicitly accesses these data sources to exclude actions and select treatments.

pith-pipeline@v0.9.1-grok · 5819 in / 1304 out tokens · 31599 ms · 2026-06-25T21:31:29.007838+00:00 · methodology

discussion (0)

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