Washington State University’s Center for Precision and Automated Agricultural Systems held a technology expo in early October to demonstrate mechanical and automated systems that are being developed to help orchardists improve the efficiency of their operations.
WSU scientists have been developing a handheld mechanical thinner that can be used to do targeted thinning that might be particularly helpful for reaching the inner canopy in large cherry trees. A former WSU graduate student, now in China, is commercializing a lightweight thinner that has a variable speed motor and operates from a battery in a backpack. Karen Lewis, WSU extension specialist, who has been testing it, hopes will be available soon. She has been also testing the Electrolit (formerly known as Electro’Flor) handheld thinner, which is manufactured by INFACO in France. The telescoping pole is made of carbon fiber, which makes it lightweight, and the battery pack fits into a special vest that the user wears.
Intelligent bin dog
Yunxiang Ye demonstrated an intelligent bin dog—an electric bin carrier that can go out into the orchard to deliver empty bins and pick up full ones. The current prototype is controlled remotely by an operator with a joystick.
Lewis, a collaborator on the national research project Comprehensive Automation for Specialty Crops, demonstrated an autonomous two-person platform. It requires no driver, and the wings of the platform can be moved in and out so the workers can reach the canopy for whatever task they are doing, such as limb tying or blossom thinning.
DBR harvesting system
Expo attendees were invited to try picking from a prototype of the DBR Conveyor Concepts’s harvesting system specially designed for use in Washington. Lewis said it had been modified in several ways since the 2011 season. Another platform was added at a different height, and it now has a creep gear and LED lights so that it can operate at night. Changes have also been made to try to reduce bruising. In tests in Washington this season, it has worked well for fruit that were firm at harvest, but with softer apples, there was more bruising, Lewis reported.
Shake and catch harvesting
Jianfeng Zhou demonstrated a shake-and-catch system for harvesting fresh cherries that he is working on with Dr. Matt Whiting. Pickers would work in pairs, with one person using a handheld, battery-operated limb shaker and the other holding a portable catching surface to catch the falling cherries and transfer them into the bin. The scientists are doing tests to find the optimal shaking frequency and duration and to evaluate the feasibility of selective harvesting based on maturity.
Real-time labor monitoring
Yiannis Apatzidis demonstrated a system for monitoring the weight of fruit each worker picks during harvest. There are two ways this can be accomplished. For the first method, an empty bin is placed on a large weighing scale in the orchard. Pickers wear RFD tags, which they scan each time they dump a bag of fruit into the bin, and the increase in weight of the bin is recorded in a computer and attributed to them. In an alternative system, the picker is weighed wearing an empty picking bag on a small weighing scale in the orchard. After filling the picking bag, the worker is weighed again before dumping the fruit into the bin. The data can be used for payroll purposes, to generate yield maps, and to analyze the productivity of workers or the picking efficiency in different blocks, for example. Riley Wortman, who is working on the software, said the system would allow growers to pay for the actual pounds of fruit picked. Currently, many growers pay by the bag or lug, but the containers are not always filled to the top.
Apple crop estimating
Dr. Manoj Karkee, assistant professor at CPAAS, described a project to develop an apple crop load estimating system. An over-the-row system incorporating color and 3D cameras and orientation sensor, would take images of both sides of a row of apple trees in order to estimate the number of fruit. The images would be processed to create 3D maps so that individual apples were not counted twice. The project is funded by the Washington Tree Fruit Research Commission. Once the fruit can be mapped, the next step would be to develop an automated harvest system, Karkee said.
Karkee also described a project to develop an image processing system that can locate branches and identify pruning points, based on observation of how humans prune apple trees, as the first step in developing an automated apple pruning system.
Jingjin Zhang is is using a mobile light sensor system to assess and map light interception in cherry orchards that have new tree training systems, such as the UFO (upright fruiting offshoots) and Y trellis. A sensor bar on the front of four-wheel orchard vehicle is driven through the orchard to map the amount of photosynthetically active radiation (PAR). Studies in high-density orchards have shown a positive relationship between PAR and yield, she reported.
Yasim Oroosh is working with Dr. Troy Peters to develop a computer-based wireless irrigation control system. Using temperature data, input from infrared sensors, and soil moisture monitors to determine the water status of the trees, the system will determine when irrigation should begin in certain parts of the orchard and for how long it should be applied. Irrigation can be turned on automatically before the trees experience moisture stress.
Scientists at CPAAS are also collaborating on the engineering aspects of a solid-set canopy delivery system for orchard sprays and working on the use of spectral imaging to determine the internal quality of apples after harvest.
Look in the November issue of Good Fruit Grower for articles about other technology projects, including a robotic pruning system and an automated crop estimating system.