Environment and precision
Every field matters. Every decision counts.
We build tools to understand the field, plan resource use and document work, with the goal of reducing avoidable waste and supporting environmental protection.
01 / Observe
Understand the area before deciding.
Boundaries, exclusions and observations help define where to investigate and where to work. In Farms, maps and satellite history give context to assessment in the field.
Captures from different dates reveal variations in vegetation and water. Studies using Sentinel-2 relate these observations to field measurements under specific conditions. A change in an index prompts a question and a field visit; it does not identify the cause on its own. Kayad et al., 2019; ESA · Sentinel-2.
Compare dates, values and legends. In Farms history, NDVI, NDMI and EVI2 use relative scales for each capture: the same color on two dates can represent different values. NDWI keeps its fixed scale. This supports monitoring without turning an image into a diagnosis or a sustainability indicator.
Timeline
5 captures
NDVI, NDMI and EVI2: relative colors per capture. NDWI: fixed scale from −1 to +1.
02 / Plan
Mathematics and physics before mobilizing resources.
Farms Atlas is our proprietary AI engine for agricultural planning and simulation. Mathematical and physical models help examine application passes, turns, returns and estimated time and resource demands for the equipment and scenario.
Planning lets you review alternatives before taking equipment to the field. Research on section control and variable-rate application shows opportunities to reduce unnecessary application in specific systems. These results concern that equipment and those conditions; they do not measure savings achieved with Farms. Luck et al., 2010; Chen et al., 2020.
A shorter route or less simulated time does not, on its own, establish lower environmental impact. Life cycle assessment considers other resources and stages, with results depending on local conditions and the assumptions used. Medel-Jiménez et al., 2024.
Wind, temperature and humidity also inform application decisions. Experiments with ground sprayers show their relationship with drift. Check forecasts and local conditions; this study does not establish a safe operating window for aircraft or drones. Nuyttens et al., 2006.
Air Tractor AT-502B
03 / Document
Evidence for the next decision.
Sofia is our AI agent for building an understanding of an area's recorded history. New records update the context, with versions and a path back to the sources supporting each observation.
In FotoTrack, location, date and recorded conditions belong to the photo. Sofia adds descriptions and suggested tags. A clear, authorized destination allows the photo to be linked to an area; when uncertain, you confirm. Interpretation stays distinct from the original evidence, and links can be reviewed.
Return to an earlier version to understand what was known at that time. Bring operation records into the report to review choices with the team and prepare the next job. This history makes decisions reviewable; demonstrating environmental improvement requires its own assessment beyond the records.
Sofia
Context for Field 04
Field 04 brings together a record of standing water on 23 October and new observations of damage to corn leaves on 25 October. The field note reports holes in leaves at three locations and asks for new photos on Friday.
The photos and note from 25 October add leaf observations to the history. They do not establish a causal link with the water recorded earlier.
Sources for this context
A commitment grounded in evidence.
Research points to environmental benefits in particular uses of precision agriculture, but adopting technology does not guarantee the same result in every field. Local conditions, skills and implementation matter. Our commitment is to provide tools to observe, plan and document better, with clarity about what each piece of evidence supports.
Ruder et al., 2026 · FAO, 2022
Studies and references
Studies, assessments and technical documentation supporting this page. The cited results do not measure Farms' impact.
Ruder et al., 2026 — Reviewing the evidence on precision agriculture and environmental sustainability
Systematic review of studies from 2000–2022. Environmental benefits depend on technology and context; it does not assess Farms.
Ceccarelli et al. · FAO, 2022 — Leveraging automation and digitalization for precision agriculture: Evidence from the case studies
International case-study synthesis. Skills, infrastructure and local suitability influence the benefits of digital tools.
Luck et al., 2010 — Potential for pesticide and nutrient savings via map-based automatic boom section control of spray nozzles
Three-field study of automatic section control on a ground sprayer. Results are specific to that equipment and comparison.
Chen et al., 2020 — Control of insect pests and diseases in an Ohio fruit farm with a laser-guided intelligent sprayer
Two-year field evaluation in four fruit crops. Compares variable-rate and constant-rate application using specialized ground equipment.
Medel-Jiménez et al., 2024 — Life cycle assessment of four different precision agriculture technologies and comparison with a conventional scheme
Life cycle assessment with modeling of a crop rotation in Austria. Results depend on scenarios, local conditions and assessment boundaries.
Nuyttens et al., 2006 — Experimental study of factors influencing the risk of drift from field sprayers, Part 1: Meteorological conditions
Experiment on weather conditions and drift from ground spraying. It does not define universal limits or a safe window for aerial application.
Kayad et al., 2019 — Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
Corn research relating satellite indices, measured yield and modeling. Relationships are specific to the site, crop and period; they do not validate diagnosis from colors.
ESA — Plant health
Undated Sentinel-2 technical documentation. Explains multispectral vegetation observations; it does not measure Farms' environmental impact.
Inamasu et al. · Embrapa, 2011 — Agricultura de precisão: um novo olhar
Brazilian scientific and technical book for further reading. Background reference, with no experimental results attributed to an unreviewed chapter.
U.S. GAO, 2024 — Precision Agriculture: Benefits and Challenges for Technology Adoption and Use
Government assessment of benefits, adoption and agricultural-data challenges. An evidence synthesis, not a field trial or endorsement of Farms.
Start by understanding your area better.
Bring observation, planning and records to your next job with Farms.