Data-driven optimal crop protection decisions to minimize pesticide residue per fruit
A precision agriculture approach that analyzes canopy architecture, spray deposition, and fruit residue data to identify the most effective crop protection treatments. By combining computer vision, Raman spectroscopy, chemometrics, and machine learning, the solution uncovers optimal spraying patterns already present in orchard data, enabling targeted pesticide application that reduces residue per fruit while maintaining pest control effectiveness.
Overview
This solution addresses a central challenge in modern orchard management: how to minimize pesticide residue on fruit without sacrificing crop protection effectiveness. Rather than relying solely on generalized spraying protocols, the approach assumes that the most effective treatment combinations may already exist within historical and real-time orchard data. By systematically analyzing canopy architecture, spray deposition patterns, and residue outcomes, the technology identifies optimal crop protection decisions tailored to specific crops and growing environments. The result is a more precise, data-driven spraying strategy that benefits growers, consumers, and the environment by reducing chemical residues on ready-to-pick fruit.
Technical specifications
Key features:
- Canopy and fruit mapping: Per-tree collection of canopy architecture, metrics, fruit counts, fruit sizes, and their spatial distribution across an apple block in a commercial orchard
- Spray deposition monitoring: A spray deposition sensor measures insecticide effectiveness per segment of ten trees, with the goal of resolution down to individual trees across each row
- Residue analysis: Spatially Offset Raman Spectroscopy (SORS) analyzes surface insecticide residues on fruit and maps hotspots of contamination
- Pattern recognition and modeling: Chemometrics and machine learning techniques, including Linear Discriminant Analysis and Support Vector Machines, identify the most effective treatment-and-result combinations hidden within the collected data
- Integrated pipeline: Combines computer vision, spectroscopy, and data modeling into a unified decision-support workflow for precision agriculture
Technology readiness level
The concept is at an early-to-mid stage of development. Validation procedures are not yet defined, but a concrete future validation plan is in place: canopy data, spray deposition measurements, and Raman-based residue mapping will be collected across a commercial New Zealand apple orchard over a growing season. The patterns revealed through chemometric and machine learning analysis will be used to confirm whether optimal crop protection decisions can be reliably extracted from existing orchard data patterns. Further field validation and broader crop trials would be needed before commercial deployment.
About Lincoln Agritech Ltd. - (Abbas Jafari)
Lincoln Agritech is a multidisciplinary research and development company, operating as a 100% subsidiary of Lincoln University, that delivers science and engineering technologies for primary industries and the environmental sector [3, 4]. Within this organization, Abdolabbas (Abbas) Jafari serves as a Precision Agriculture Scientist and Research Scientist [1, 2, 3]. His work focuses on integrating computer vision, machine learning, data modelling, and Raman spectroscopy to optimize farming practices, enhance crop production, and advance sustainable agriculture [1, 5]. By applying these advanced scientific tools, the team aims to solve complex challenges related to water, land-use, and bio-resources [1, 4].
These research efforts provide practical benefits to farmers, consultants, and veterinarians by improving agricultural efficiency and environmental sustainability [2, 6]. For example, Jafari has been involved in investigating the application of augmented reality in dairy farming to assess potential industry benefits [6]. By leading projects that bridge the gap between engineering and real-world agricultural problems, the team supports the development of innovative, evidence-based solutions that help crops thrive while reducing the environmental footprint of primary industry operations [1, 2, 6].
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