Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

Abstract

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project's main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications-insect monitoring, crop load scouting, and caliper measurement-and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.

Modeling and Calibrating Visual Yield Estimates in Vineyards

“…Other recent small scale experiments in vineyard work is Dey [3] present a method for classifying plant structures, such as the fruit, leaves, shoots based on 3D reconstructions generated from image sequences which unlike our work is sensitive to slight wind whilst imaging. Other crop detection based on computer vision methods using color pixel classification or shape analysis has been attempted on various fruit types -Jimenez et al [7] provides a summary of fruit detection work, Singh et al [10] present a method for detecting and classifying fruit in apple orchards and Swanson et al [11] use the shading on the curved surfaces of oranges as a cue for detection.…”

Yield estimation in vineyards by visual grape detection

“…The method would not be applicable for the majority of real world examples where the fruit appears over a background of similarly-colored leaves, as is the case in white grape varieties and in all varieties before véraison. More complex crop detection based on computer vision methods using color pixel classification or shape analysis has been attempted on various fruit types -Jimenez et al [5] provides a summary of fruit detection work, Singh et al [9] present a method for detecting and classifying fruit in apple orchards and Swanson et al [10] use the shading on the curved surfaces of oranges as a cue for detection.…”

Autonomous Orchard Vehicles for Specialty Crops Production

“…We equipped Laurel with sensors, onboard computers, and a software suite to enable the vehicle to drive between rows of trees and turn around at the end of rows Singh et al, 2010). We also showed how such vehicle could be used to tow a mower or carry a sprayer and thus execute typical orchard maintenance operations.…”