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Identifying runners can be achieved through a variety of methods. Initially, the CPSC tried traditional image processing techniques such as color segmentation, edge detection, and bandpass filter methods to identify runners by considering parameters like color, size, shape, and texture. However, the accuracy of each of these image-processing techniques depends on plant variety, time of year, and farm location. As a result, we decided to take a machine-learning approach to identify runners and differentiate them from stems. This approach utilized models trained on a wide range of image data from different field conditions and plant varieties. Due to the similarity between runners and plant stems as well as the crowded nature of strawberry canopies, common object detection networks like region-based convolutional neural networks (R-CNN) and YOLO were not chosen. Instead, we used a two-step framework for runner detection from RGB images. First, the segmentation network identified the runner pixels as a runner mask image, then an object detection network was applied to the created mask image to recognize and localize different instances of runners. With the combination of these two networks, we were able to achieve instance segmentation for runners, as shown in Figure 4.

In fruit production fields, runners must be removed to minimize the plants' competition over limited resources, which helps to promote flower and fruit production. Most commonly grown strawberry varieties like Monterey, Fronteras, Cabrillo, and Portola produce lots of runners during the berry growing season, which means growers must bear a significant production cost to manage their fields in order to generate higher fruit yields. Figure 2 shows the labor intensity of conventional runner cutting.

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In order to provide the data needed to train and test our model, we collected image data from two strawberry fields in the Watsonville/Salinas area with the help of Strio AI's camera system and robotic platform as well as CPSC's tractor-mounted enclosed data collection platform (Figure 5). These data were collected at the end of July 2021 from three strawberry varieties (Monterey, Cabrillo, and Albion). The first two varieties were from the field in Salinas, and the last variety was from the field in Watsonville. All plants were conventionally planted in Fall 2020.

In December 2020, Dr. Ruijie He, CEO and co-founder of Strio AI, began visiting CPSC as well as strawberry fields in California and Florida to better understand runner cutting and its challenges. Dr. He and his team were able to develop the first prototype of a robotic runner cutter and test it in the strawberry fields in California and Florida. This robot was able to maneuver throughout the strawberry fields and perform runner-cutting tasks without causing any damage to the plants or fruits. This robot will need more improvements before it becomes commercially ready to use in strawberry fields. However, in March 2022, Strio AI joined Zoox, which is an Amazon subsidiary company that develops autonomous vehicles, and they are no longer active in the strawberry automation field.

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In this study, for each cultivar, 80% of the collected data were randomly selected for training, and the remaining 20% were considered for testing. The results indicate that machine-learning methods are able to detect runners with high accuracy, as demonstrated in Table 2.

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During the data collection process, we used a color camera to capture still images at 5 frames per second with 1024×768 pixel resolution and set the exposure time as stated in Table 1. The exposure times were set differently based on travel speed to allow the camera more light absorption while avoiding blurriness on the images. Figure 6 shows examples of the images that were captured.

To prepare the data for training and testing, runners in the images were labeled by using the semantic segmentation labeling tool of Amazon Web Service (AWS) SageMaker. Bounding boxes were placed around each runner’s label. Google Cloud Platform (GCP) AutoML Vision Object Detection was utilized to train an object detection model. This allowed us to predict runners and put bounding boxes around them for counting purposes.

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Even though runner removal is essential for maintaining the healthy condition of fruit production fields, most growers are having a hard time performing it as frequently as needed due to labor shortages in the agricultural industry. In some cases, growers might have their field workers prioritize picking fruits and ignore runner cutting for some consecutive months. Figure 3 shows an extreme case of runner propagation in a fruit production field. An autonomous runner-cutting machine can help growers address this problem without sacrificing strawberry production.

Figure 5. Data collection platform on a tractor (left) and Strio AI robotic platform (right) (July 2021, Watsonville and Salinas).

For Monterey, Cabrillo, and Albion, the best results for IoU and BF Score were 53% and %87%, 56% and 89%, and 72% and 95%, respectively. Even though the results between different trained models for each cultivar were similar to each other, the deep network was able to detect runners for the Albion variety with higher accuracy compared to the Monterey variety. This accuracy difference can be related to the different data collection and ambient conditions between the two varieties as indicated in Table 1.

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Strawberry plants develop “runners”, also called “stolons”, for propagation in response to longer days and warmer temperatures. These runners are like stems but grow over the soil surface, which can extend out several inches from the crown, then take root in the soil and result in clone plants called “daughter plants” (Figure 1).

Figure 3. An extreme example of runner propagation in a fruit production field (Credit: Dr. Gerald Holmes; July 2021, Santa Maria).

Cal Poly Strawberry Center - Automating the California Strawberry Industry Speaker: John Lin, Ph.D., Director of Automation Engineering, California Strawberry Commission/Cal Poly Strawberry Center

By Mojtaba Ahmadi, Ph.D., Senior Production Automation Engineer | Published: October 13, 2022 | Updated: November 14, 2023

Based on this study, runner detection in strawberry fields can be performed by taking advantage of deep-learning models. However, in order to optimize the performance of these models, further studies are needed to improve data collection conditions and fine-tune deep-learning models.

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According to the 2021 Cost Study of Strawberry Production, runner removal is the second-most labor-intense operation in strawberry production. It is estimated to take 12.5 hours per acre per month during 10 months of the production season, which results in an approximate labor cost of $2,683 per acre. Given that California has 38,026 planted acres of strawberries as reported in 2022, the total cost of runner removal is over $100 million for the industry.

The performance of a deep-learning framework to detect runners using RGB images depends on the quality of the acquired images. During the image collection process, the project faced several challenges that should be addressed for future improvements. These are listed below:

Figure 2. Conventional runner cutting is a tedious, labor-intense operation in strawberry production (July 2020, Santa Maria).

Since 2020, the California Strawberry Commission (CSC) and the Cal Poly Strawberry Center (CPSC) have been investigating the potential use of automated runner cutters in the field. Our engineering team has developed a deep-learning framework to detect and identify runners from other strawberry plant organs by using RGB image data. In addition, the CPSC began a collaboration with Strio AI, a robotics start-up company based in Boston, MA, to develop an automated runner cutter. As part of their efforts, Strio AI built a robotic platform that was able to easily maneuver in strawberry fields, and with the use of a robotic arm and the fusion of RGB and depth data, it was able to successfully cut runners in the field.

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Our precision components are ideal for a wide range of manufacturers who face challenges related to wear and corrosion. The carbide tooling and wear parts we produce for metal forming, material transfer and fluid control applications includes:

While many of our competitors limit quality assurance (QA) to their inspection departments, we take a better approach. At Dura-Metal, we believe that QA starts before we receive the order and continues after we ship the product. Specifically, we perform visual and dimensional inspections on all incoming materials and check workpieces at every stage of production. We then follow up with customers regularly to make certain that our parts and tooling meet or exceed specifications.

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Part of this project was to collaborate with the private sector on the development of an automated robotic runner cutter. Our team approached multiple robotics companies, and Strio AI, a Boston-based robotics start-up company, proposed a robotic solution for the laborious task of runner cutting.

FIRA USA 2023 - Panel Discussion "Practical translation of R&D and technology to the California strawberry industry", Sept 19th, 2023 Invited Speaker: Mojtaba Ahmadi, Ph.D., Senior Production Automation Engineer, California Strawberry Commission/Cal Poly Strawberry Center