Advancing Robotic Approaches to Precise Apple Crop Load Management

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Commodities

• Fruits: apples

Practices

• Crop Production: other, pollination

Crop load management is the single most important yet difficult management strategy that determines the annual profitability of apple orchards. Pollination and thinning are the two aspects that affect greatly on effective crop load management. Environmental conditions interfere with the natural pollination process thus causing huge uncertainty in achieving optimal pollination. Later after the pollination process, to apply appropriate amount of thinning remains a challenge: If thinning is inadequate and too many fruits remain on the tree, fruit size will be small, whilefruit quality will be poor. Over thinning also carries economic perils since yield and crop value the year of application will be reduced and fruit size will be excessively large with reduced fruit quality due to reduced flesh firmness, reduced color and a much-reduced post-harvest life. Thus, in this study, we propose to develop a robotic apple crop load management system to achieve high yield and quality of fruit crops resulting in a substantial economic benefit to the tree fruit industry. The proposed robotic system consists of two major components; i) a well-developed machine vision system that can identify the location of apple flower cluster and king flowers, generate flower density map, and communicate with robotic manipulator automatically; and ii) a spraying system that is mounted on a robotic manipulator to reach target position. It is expected that our prototype and field validation will provide sufficient information for companies to develop and commercialize a robotic crop load management system for the growers.

The primary goal of this proposed project is to develop an autonomous apple crop load management system that is capable of localization of flower clusters and king blossom on apple canopies to perform precision targeted spraying. The project will be focused on the following objectives:

1. Establish a flower cluster image dataset throughout the flowering growing stage

The purpose of this objective is to collect adequate dataset of apple flowers for the lateral training and testing for the Deep Learning network. Images will be taken on the Gala and Honeycrisp apple trees during different periods of flowering stages. Two ZED 2 stereo cameras (Stereo Labs) will be used to cover whole tree canopy. The manually-counted number of king blossom will be served as the ground truth to be compared with the results of vision system.

2. Flower Cluster Localization and King Blossom Identification

In this objective, a deep learning-based machine vision system will be developed for individual apple flower segmentation. With some further image processing process, separate flower clusters will be localized. King blossom will be identified within each flower cluster.

3. Develop a path sequencing algorithm to support the decision making for chemical suspension

An optimized trajectory leads to higher efficiency for the robotic system. The most well-known problem of searching for the optimal sequence is the Traveling Salesman Problem (TSP). The goal of the TSP is to find a minimal-cost cyclic tour through a set of points such that every point is visited once.

4. Integrate the vision system and robotic manipulator to perform lab evaluation on precision apple flower pollination

A UR-5e robotic manipulator will be used to integrate general spraying nozzle along with the vision system developed in previous objective. The manipulator and sensors will be mounted at different locations and orientations in a reconfigurable frame to make it suitable to fruiting-wall canopy architectures.