--- title: >- John Deere (Deere & Company): In-season post-emergence herbicide application (See & Spray targeted spraying) slug: john-deere-see-and-spray stable_id: caa3124cf4457913 company: John Deere (Deere & Company) function_code: agriculture pattern_codes: - threshold_as_control - decision_right_transfer evidence_strength: mixed publication_tier: showcase freshness: current reviewed_at: '2026-08-23' updated_at: '2026-08-22' source_quality_summary: 1 independent, 1 primary; publication outcomes are verified and reported. caveat_summary: >- Company-reported and company-calculated. Deere's footnotes disclose the basis honestly: the 59% is 'Compared to broadcast spraying and based on use of See & Spray Premium and Ultimate', and the 8 million gallons is 'Calculated by using an average of 15 gallons per acre for nonresidual herbicide product applied' - so the gallons figure is a derivation from an assumed application rate, not a measured volume, and Deere labels it 'estimated'. The saving applies to non-residual (post-emergence) product only. Independently corroborated in direction and magnitude by a three-year University of Arkansas field trial finding a 43-59% reduction versus broadcast, though that trial covers Arkansas soybeans only and used a prototype machine. This is an independent land-grant university measurement, not a collections: - human-still-decides - embodied-work - negative-results bundle_version: 1.0.0 bundle_fingerprint: sha256:c23c6cc2b88153d008ea8fda928f632ce0011fc2d4c5036672a16e5d895bab93 canonical_url: https://brianletort.ai/transformations/john-deere-see-and-spray --- # John Deere (Deere & Company): In-season post-emergence herbicide application (See & Spray targeted spraying) That a sprayer cannot tell a crop plant from a weed while moving through a field, so the only reliable way to control weeds is to broadcast herbicide across the entire field regardless of where weeds actually are. Function: Agriculture. Patterns: Threshold as the human control; Decision-right transfer. Evidence: mixed. Freshness: current. Reviewed: 2026-08-23. Updated: 2026-08-22. Source quality: 1 independent, 1 primary; publication outcomes are verified and reported. ## Before 1. **Grower / agronomist** — Decides the post-emergence herbicide programme for the field based on expected weed pressure across the whole area. (control: Label rates, agronomic recommendation, whole-field application plan.) 2. **Sprayer operator** — Drives across the field applying herbicide uniformly because the sprayer cannot distinguish crops from weeds. (control: Boom height, speed and nozzle calibration; no plant-level targeting is possible.) 3. **Field** — Receives herbicide across the treated area, including ground with no weeds. (control: Chemical cost and environmental load scale with acres, not with weed presence.) 4. **Grower** — Pays for herbicide across every acre sprayed and absorbs crop injury from herbicide applied to unweeded ground. (control: Input budget.) ## After 1. **Grower / agronomist** — Sets the spray threshold that determines how aggressively detected plants are treated as weeds. (control: Sensitivity setting; independent research finds the high sensitivity setting is best in most scenarios and that a low setting increases the weed seed bank.) 2. **See & Spray camera and vision system** — Scans over 2,100 square feet per second at up to 15 mph while the sprayer moves. (control: 120 ft boom camera array; ExactApply nozzle hardware.) 3. **Onboard machine-learning processors** — Determine plant by plant whether each detected plant is a crop or a weed, and issue commands to individual nozzles - the application decision moves from the operator's whole-field plan to a per-plant machine judgement made in real time. (control: The grower's sensitivity threshold is the operator-set boundary within which the model decides; no per-plant human review is possible or attempted.) 4. **ExactApply nozzles** — Deliver a precise dose of herbicide only where a weed is recognised, leaving unweeded ground untreated. (control: Individual nozzle actuation.) 5. **Grower / agronomist** — Monitors weed escapes and seed-bank consequences across seasons and adjusts sensitivity, residual herbicide strategy and pass structure accordingly - a new agronomic control loop that did not exist under broadcast application. (control: Independent guidance to broadcast residual herbicides and target only post-emergence chemistry; multiple passes required on the Premium configuration.) ## Decision rights The per-plant application decision transfers to the onboard model. The grower retains authority over the sensitivity threshold that bounds the model's behaviour, the herbicide programme, and the pass structure. This is a genuine transfer of an operational decision that was previously made once per field by a human and is now made thousands of times per second by a model, with no possibility of per-decision human review. The boundary is set once per pass rather than negotiated per case, which makes the threshold a control of unusual leverage - independent research quantifies the consequence of setting it wrongly. ## Exception path Agronomic rather than transactional. Independent University of Arkansas research establishes the required exception handling: residual (pre-emergence) herbicides should be broadcast rather than targeted, and only post-emergence chemistry should be targeted. On the single-tank Premium configuration this forces the grower either to apply residuals through the targeted system or to make two separate passes. Weed escapes surviving a targeted pass cannot be controlled by a follow-up application and must be managed as a seed-bank problem in subsequent seasons. Deere's commercial exception path is financial rather than operational: customers pay only for acres where the technology is used, and from 2025 an Application Savings Guarantee means customers pay for the technology only when an application saving is achieved. ## Outcomes - **Herbicide volume applied per acre versus whole-field broadcast application** (reported): Broadcast spraying - herbicide applied uniformly across the entire field, calculated by Deere at an average of 15 gallons per acre of non-residual herbicide product applied → Average herbicide savings of 59%, and an estimated 8 million gallons of herbicide mix saved. Company-reported and company-calculated. Deere's footnotes disclose the basis honestly: the 59% is 'Compared to broadcast spraying and based on use of See & Spray Premium and Ultimate', and the 8 million gallons is 'Calculated by using an average of 15 gallons per acre for nonresidual herbicide product applied' - so the gallons figure is a derivation from an assumed application rate, not a measured volume, and Deere labels it 'estimated'. The saving applies to non-residual (post-emergence) product only. Independently corroborated in direction and magnitude by a three-year University of Arkansas field trial finding a 43-59% reduction versus broadcast, though that trial covers Arkansas soybeans only and used a prototype machine. - **Herbicide use reduction versus broadcast application, independently measured** (verified): Broadcast application across the entire plot area → 43% to 59% reduction in herbicide use. This is an independent land-grant university measurement, not a restatement of Deere's figure, which is what makes it valuable - but its scope is narrower than the commercial claim. It covers soybeans in one US state, and the machine used was a Blue River Agricultural Test Machine, a scaled version of See & Spray Ultimate, operated as a single-tank system to simulate the more widely adopted Premium configuration. It is therefore corroboration of the mechanism and magnitude rather than verification of the commercial product's performance across corn, soybean and cotton. - **Per-acre economic return at the high sensitivity setting** (verified): Broadcast application method, assuming a $25,000 See & Spray upgrade cost from a comparable John Deere sprayer and incorporating applicator efficiency → Saved $30.49 per acre over the entire season with no increased risk relative to the broadcast method; the sprayer pays for itself after treating 819 acres for a $25,000 upgrade or 2,460 acres for a $75,000 upgrade if used at both early and mid post-emergence in a single season. An independent economic analysis by a PhD researcher, based on stated assumptions about upgrade cost and applicator efficiency, and specific to Arkansas soybeans. It is a modelled return under one configuration and one crop, not a realised financial result, and the authors were still producing additional scenarios for other crops and cost structures at time of publication. The 'no increased risk' finding applies specifically to the high sensitivity setting. - **Palmer amaranth pigweed population change at the low sensitivity setting** (verified): Weed population under broadcast application → Increased by 280% each year, because only larger weeds were sprayed and the escapes produced seed, increasing the soil seed bank. This is counter-evidence and is recorded as an outcome deliberately, not buried. It is independently measured and material: the same technology that delivers a 43-59% herbicide reduction can multiply the weed seed bank nearly fourfold per year if the grower sets sensitivity low. Critically, the researcher attributes the mechanism to the operator's configuration choice, not to a failure of the vision model - 'When a grower lowers the sensitivity setting, only larger weeds are sprayed' - which makes this a governance and configuration lesson rather than a system defect. Arkansas soybeans only. Researchers estimate improper use could cost Arkansas producers upwards of $60 million per year through accelerated herbicide resistance or additional in-season applications. ## Executive lesson The transferable lesson is not the 59% saving, it is what determines whether you get it. Independent Arkansas research found the same system delivering a 43-59% herbicide reduction and $30.49 per acre at high sensitivity, and a 280% annual increase in pigweed at low sensitivity - and the researcher attributes that difference to the grower's threshold setting, not to the model misclassifying weeds. When you move a decision to a model, the human control usually collapses into a small number of configuration choices made before the work starts, and those choices carry consequences the operator will not see for a season or more. Most organisations govern the model and leave the threshold to whoever runs the machine. This case is also unusually well evidenced for a commercial AI claim: a land-grant university measured it independently, published counter-evidence, and estimated that improper use could cost Arkansas producers more than the midpoint of the potential savings. Deere's response to that uncertainty is worth noting - it priced per acre used and guaranteed savings, moving the risk onto itself rather than the buyer. ## Anti-pattern Optimising the machine's threshold for the immediate, visible metric. Setting See & Spray to low sensitivity produces exactly what a per-pass cost report rewards - the least herbicide used - while multiplying the Palmer amaranth seed bank by 280% each year, escapes that a follow-up application cannot control, and accelerated herbicide resistance. The researchers estimate improper use could cost Arkansas producers upwards of $60 million per year against modelled savings of $1.6 million to $48 million, so the downside can exceed the upside. The second anti-pattern is treating the vendor's headline as the whole finding: Deere's 59% figure is honestly footnoted to broadcast spraying, non-residual product and specific machine configurations, and independent research separately establishes that residual herbicides should be broadcast rather than targeted, which requires multiple passes on the widely adopted Premium configuration. Quoting 59% without the configuration and pass-structure conditions overstates what a buyer will experience. ## Questions for leaders - Where have we moved a high-frequency decision to a model, and who owns the threshold that bounds it? - Does the person setting that threshold see the consequences of setting it wrongly, and on what time horizon? - Which of our AI metrics would improve if someone configured the system for the short-term number at the expense of a second-order outcome? - Do we have any independent measurement of an AI outcome we are citing, or only the vendor's figure and our own? - What is our equivalent of the seed bank - the slow-accumulating consequence our primary metric will not show us for a year? - Would our AI vendors price on realised outcome rather than on capability, and have we asked? ## Sources - [See & Spray Customers See 59% Average Herbicide Savings in 2024](https://www.deere.com/en-us/john-deere-news/see-spray-59-percent-herbicide-savings) — John Deere (Deere & Company) - [Precision agriculture research measures effectiveness of See & Spray technology](https://www.uaex.uada.edu/media-resources/news/2025/march/03-10-2025-see-and-spray-research.aspx) — University of Arkansas System Division of Agriculture, Arkansas Agricultural Experiment Station