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Top Strawberry Computer Vision Models
The models below have been fine-tuned for various strawberry detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own strawberry detection model.
At the bottom of this page, we have guides on how to count strawberries in images and videos.
3673 images1 model
grape leafApple Scab LeafApple leafApple rust leafBell_pepper leafBell_pepper leaf spotBlueberry leafCherry leafCorn Gray leaf spotCorn leaf blightCorn rust leafPeach leafPotato leafPotato leaf early blightPotato leaf late blightRaspberry leafSoyabean leafSquash Powdery mildew leafStrawberry leafTomato Early blight leaf
2556 images3 models
grape leafApple Scab LeafApple leafApple rust leafBell_pepper leafBell_pepper leaf spotBlueberry leafCherry leafCorn Gray leaf spotCorn leaf blightCorn rust leafPeach leafPotato leafPotato leaf early blightPotato leaf late blightRaspberry leafSoyabean leafSoybean leafSquash Powdery mildew leafStrawberry leaf
66211 images2 models
Apple black rotApple cedar rustApple healthyApple scabBasil Cercospora leaf spotBasil Downy MildewBasil Fusarium WiltBell pepper bacterial_spotBell pepper healthyBlueberry healthyCassava bacterial blightCassava brown leaf spotCassava healthyCassava mosaicCassava root rotCherry healthyCherry powdery mildewCilantro-Bacterial Leaf SpotCilantro-Blue SpotCitrus haunglongbing
2440 images1 model
grape leafApple Scab LeafApple leafApple rust leafBell_pepper leafBell_pepper leaf spotBlueberry leafCherry leafCorn Gray leaf spotCorn leaf blightCorn rust leafPeach leafPotato leafPotato leaf early blightPotato leaf late blightRaspberry leafSoyabean leafSquash Powdery mildew leafStrawberry leafTomato Early blight leaf
3774 images1 model
B Natural Apple FiberB Natural Guava FiberB Natural Guava Fiber 180mlB Natural Litchi FiberB Natural Mango BitzB Natural Mango FiberB natural mixed fruitBabool Tooth Paste 175g +175gBabool Toothpaste 175gmCpBaidyanath chyawanprashBourn vita bottleBourn vita pouchCavins chocolate milk shake 200 mlClose upColgate Charcoal CleanColgate VedshaktiColgate dental creamColgate max freshColgate ved shaktiDabur Chyawanprash
172 images2 models
BUD LIGHT CHELADA MANGONADA 1/25C Electrolit Strawberry Banana 21oz Monster Energy Drink Juice Aussie Style Lemonade 16oz Red Bull Amber Edition Strawberry Apricot 8.4oz Simply Orange with Mango 11.5oz7S Alkaline Water 1L7S Farmers Grove Apple Juice 117S Farmers Grove Apple Juice 11.5oz7S Farmers Grove Cranberry 117S Farmers Grove Cranberry 11.5oz7S Farmers Grove Lemonade 117S Farmers Grove Lemonade 11.5oz7S Farmers Grove Orange Juice No Pulp 117S Farmers Grove Orange Juice No Pulp 11.5oz7S Farmers Grove Raspberry Lemonade 117S Farmers Grove Raspberry Lemonade 11.5oz7S Iced Cappuccino Mocha 12oz7S Iced Cappuccino Salted Caramel 12oz7S Iced Cappuccino Vanilla 12oz7S Organic Cold Pressed Berry Banana Bliss 14oz
Guide: How to Count Strawberries with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count strawberries in your images and videos.
The following code snippet counts the number of strawberries present in an image.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import supervision as sv
import roboflow
roboflow.login()
rf = roboflow.Roboflow()
# replace with the strawberry project you choose above
project = rf.workspace("dmitri-kaslov-fmitx").project("grocery1")
strawberry_model = project.version(1).model
results = strawberry_model.predict("strawberry.jpg").json()
strawberries = sv.Detections.from_roboflow(results)
# print number of strawberries
print(len(strawberries))Guide: How to Count Strawberries in a Zone
With a bit more code, you can count the number of strawberry present in a specific zone of your image or video.
The following code snippet counts the number of strawberry present in each frame in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
Read our blog post on counting objects in a zone
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "strawberry.mp4"
TARGET_VIDEO_PATH = "strawberry_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your shape
polygon = np.array([
# draw 50x50 box in top left corner
[0, 0],
[50, 0],
[50, 50],
[0, 50]
])
roboflow.login()
rf = roboflow.Roboflow()
# replace with the strawberry project you choose above
project = rf.workspace("dmitri-kaslov-fmitx").project("grocery1")
strawberry_model = project.version(1).model
# create BYTETracker instance
strawberry_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create PolygonZone instance
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=(video_info.width, video_info.height))
# create box annotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
colors = sv.ColorPalette.default()
# create instance of BoxAnnotator
zone_annotator = sv.PolygonZoneAnnotator(thickness=4, text_thickness=4, text_scale=2, zone=zone, color=colors.colors[0])
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = strawberry_model.predict(frame).json()
strawberries = sv.Detections.from_roboflow(results)
# show strawberry detections in real time
print(strawberries)
# tracking strawberry detections
strawberries = strawberry_tracker.update_with_detections(strawberries)
annotated_frame = box_annotator.annotate(scene=frame, detections=strawberries)
annotated_frame = zone_annotator.annotate(scene=annotated_frame)
# return frame with box and line annotated result
return annotated_frame
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)Guide: How to Track Strawberries Crossing a Line
You can count how many strawberries have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of strawberries that cross a line in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "strawberry.mp4"
TARGET_VIDEO_PATH = "strawberry_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your line
LINE_START = sv.Point(0, 300)
LINE_END = sv.Point(800, 300)
roboflow.login()
rf = roboflow.Roboflow()
# replace with the strawberry project you choose above
project = rf.workspace("dmitri-kaslov-fmitx").project("grocery1")
strawberry_model = project.version(1).model
# create BYTETracker instance
strawberry_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create LineZone instance, it is previously called LineCounter class
line_zone = sv.LineZone(start=LINE_START, end=LINE_END)
# create instance of BoxAnnotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
# create instance of TraceAnnotator
trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
line_zone_annotator = sv.LineZoneAnnotator(thickness=4, text_thickness=4, text_scale=2)
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = strawberry_model.predict(frame).json()
strawberries = sv.Detections.from_roboflow(results)
# show strawberry detections in real time
print(strawberries)
# tracking strawberry detections
strawberries = strawberry_tracker.update_with_detections(strawberries)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=strawberries
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=strawberries
)
# update line counter
line_zone.trigger(strawberries)
# return frame with box and line annotated result
return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)