$25,875 Fertilizer Savings using PIX4Dfields
This update builds on our earlier article “Towards sustainable farming: cost-saving fertilization.” Environmental Land Management Science (ELMScience), founded by Jim Fletcher and Chuck Petz, launched a project with a straightforward but ambitious goal: determine whether drone-captured data could accurately measure nitrogen (N) levels in St. Augustine grass. If successful, the data would enable growers to apply fertilizer precisely where—and only where—it was needed.
From drone data to real-world impact
Over the past 2.5 years, the team collected thousands of drone images and paired them with tissue samples from St. Augustine sod. This rigorous dataset now empowers growers to: • Apply fertilizer more precisely • Save significant input costs • Reduce environmental impact by minimizing excess application
The project unfolded in two phases:
- Validation Phase (Year 1):
• Developed a custom reflectivity index from drone imagery. • Confirmed strong correlation between drone-derived index values and lab-tested nitrogen levels.
- Implementation Phase (Year 2):
• Applied findings in the field using PIX4Dfields software. • Generated precision application maps that guided fertilizer use. • Documented measurable savings in both cost and tonnage.
Cost savings: a confirmed $25,875 reduction in fertilizer spend
ELMScience’s real-world trials have validated the power of custom indices to generate variable-rate nitrogen (N) prescriptions. Over a six-month period, the results were clear:
• Average N reduction: 23 lb/acre • Total fertilizer saved (3,000 acres): 34.5 tons • Cost savings (at $750/ton): $25,875
These financial gains are matched by environmental benefits. By reducing excess fertilizer application, growers not only protect their bottom line but also safeguard soil and water quality—ensuring healthier, more sustainable turf management.
Using PIX4Dfields’ Targeted Operations over six months on 18 acres demonstrated direct fertilizer savings of 23 lb/acre using the team’s nutrient index. If scaled across the grower’s full 3,000-acre operation, this translates to a potential reduction of 34.5 tons over the 6-month period—a savings with both economic and environmental impact. On the strength of these results, the grower has invested in a drone and PIX4Dfields to implement the index across his entire farm this year, validating its practicality and positioning it for rapid adoption.
Nutrient prediction with spectral analysis and machine learning
The research team’s technical analysis delivered major breakthroughs in nutrient prediction capabilities. By applying Random-Forest machine learning models, the algorithms achieved nutrient assessment accuracy that closely approaches laboratory standards—marking a significant step forward in precision agriculture.
Key findings from the spectral analysis include:
• Red-Edge Band: The most powerful predictor, particularly effective for estimating Phosphorus (P) levels. • Near-Infrared (NIR) Band: Consistently improved overall accuracy across all measured nutrients, reinforcing its value in multispectral analysis. • Green Band: Demonstrated specialized effectiveness for predicting Nitrogen (N) and Potassium (K), though it showed weaker correlation with Phosphorus.
Together, these insights confirm that spectral data, when paired with advanced machine learning, can deliver nutrient predictions with near-lab precision. This capability enables growers to make more informed fertilizer decisions, reduce costs, and minimize environmental impact.
Future focus: expanding nutrient algorithms to new crops
Building on the success with St. Augustine grass, current research indicates that specific light wavelengths can reveal a plant’s nutrient status with remarkable accuracy. This insight is driving the team to broaden its scope, applying the algorithms to diverse crops—including potato, almond, tomato, bahia grass, and grapes____text in bold—to validate effectiveness across different agricultural systems.
The next phase of development centers on delivering a nutrient-prediction toolkit designed for seamless adoption by growers and agronomists. Key features include:
• Automated, map-ready layers for precision nutrient management. • In-season alerts that flag emerging deficiencies before they impact yield. • Integration with standard agronomic workflows, ensuring compatibility with existing practices and platforms.
“Using PIX4Dfields to assess nitrogen levels in St. Augustine grass has delivered remarkable precision, enabling growers to reduce fertilizer inputs while maintaining turf quality. This technology provides both economic savings and environmental benefits, making it a valuable tool for sustainable sod production." - Jim Fletcher, Owner of ELMScience
By expanding crop coverage and embedding predictive tools directly into decision-making processes, ELMScience is positioning nutrient algorithms as a cornerstone of sustainable, cost-efficient agriculture.
