Decision Support Tool for Precision Orchard Management
Author: Joseph Davidson
Published: 2024
Summary: Keywords: Precision fertilization, variable rate technology, computer vision, deep learning. The standard practice of broad-acre orchard management does not result in targeted actions that are optimal for individual trees – this reduces the impact of management decisions and wastes resources while falling short on achieving the yield and quality potential of individual blocks. The primary goal of this project was to improve fruit quality and yields by managing the Nitrogen (N) of individual trees through a combination of automated sensing, machine learning, decision support tools, and variable rate application technology. To accomplish our goals, we created three specific research objectives: 1. Develop a ground vehicle-mounted sensor system that i) maps the geographic location of individual trees within an orchard block; and ii) measures plant parameters (e.g. shoot vigor, trunk cross-sectional area, and fall leaf color) to estimate the N status of individual trees 2. Develop a decision support tool that recommends N application levels per tree and tracks the tree’s long-term response 3. Develop and demonstrate a proof-of-concept precision spray system that localizes the vehicle with the orchard map, identifies the neighboring trees, and then selectively applies the desired level of N within the root zone
