Top Agriculture Computer Vision Datasets in 2026: Crop & Weed Segmentation, Plant Phenotyping, 3D Point Clouds, and Precision Farming Data

A field guide to 14 open agriculture computer vision datasets, organized by task and label type - crop/weed segmentation, orchard and fruit detection, plant disease classification, 3D point clouds, satellite field mapping, and robotics - with the exact media type, label fields, and a direct Hugging Face link for each one, so you know what’s actually inside before you download anything.

A field doesn’t look like ImageNet. The lighting changes hour to hour, a crop and the weed choking it can be the same shade of green, and the “object” you’re detecting today is a two-leaf seedling that will be a shoulder-high plant in six weeks.

Most general-purpose vision models have never seen anything like it. That’s exactly why agricultural computer vision needs its own datasets, and why so many of the best ones are stranger than a typical detection benchmark: point clouds instead of pixels, five spectral bands instead of three, robots that leave the frame and come back expecting you to remember who they were.

Sensors are cheap now - RGB-D rigs, five-band multispectral drones, RTK-GPS UAV LiDAR - and the harder problem has shifted to what to do with everything they capture.

We pulled together 14 datasets, all currently mirrored in FiftyOne format on the Voxel51 Hugging Face org, spanning row-crop weed segmentation, orchard phenotyping, plant disease classification, 3D point clouds, satellite-scale field mapping, and a robot that has to recognize a lettuce it met five minutes ago.

Below, each dataset gets a self-contained blurb - name, task, size, license, year - plus its exact FiftyOne media type, its label fields and their types, a short field list, and a direct link to the dataset on Hugging Face, grouped by exactly what kind of dataset you’re looking for, before we close by laddering up to where a lot of this data is really headed: physical AI.

Key Takeaways

14 datasets, 6 media/sensor types - RGB image, RGB-D image, 5-band multispectral image, Sentinel-2 satellite image, 3D point cloud, and video - and 8 distinct FiftyOne label types across them: Classification, Classifications, Detections, Detection, Segmentation, Keypoints, Keypoint, and Heatmap.

• Looking for a crop-and-weed segmentation dataset? Start with CropAndWeed, WeedsGalore, PhenoBench, or WE3DS - each pairs pixel-level crop/weed masks with a different sensor (ground RGB, UAV multispectral, UAV RGB, and RGB-D, respectively).
• Looking for a plant phenotyping or growth-stage dataset? AppleGrowthVision, GrowliFlower, and TomatoMAP all tie imagery to a BBCH or measured-trait timeline, from bud break through fruit maturity.
• Looking for a 3D point cloud dataset for agriculture? Pheno4D, Crops3D, and VineLiDAR skip pixels entirely and ship raw geometry - exactly the ground truth a field robot’s depth sensor or LiDAR unit needs, not just its camera.
• Three datasets are FiftyOne grouped datasets with multiple slices per sample - AppleGrowthVision (left / right / image), GrowliFlower (pre / post), TomatoMAP (pos_1-pos_4 / macro), and Fields of the World (window_a / window_b) - meaning each “sample” is really a synchronized set of related images, not a single standalone one.
• Licensing is genuinely mixed - CC BY 4.0 is common, but CropAndWeed is non-commercial-only, PlantWild is CC BY-NC-ND, and Fields of the World inherits 24 different national licenses depending on the country tile. Check before you train a commercial model.

The Comparison Table

Dataset Task Size Modality License Year CropAndWeed Detection, segmentation, keypoints 8,034 images, ~112K instances RGB (ground-level) Custom non-commercial 2023 WeedsGalore Semantic + instance segmentation 156 tiles, ~78 instances/tile UAV multispectral (R/G/B/NIR/RE) CC BY 4.0 2025 PhenoBench Semantic, instance & panoptic segmentation 2,872 images UAV RGB CC BY-SA 4.0 2024 WE3DS Semantic segmentation 2,568 images RGB-D (stereo) CC BY 4.0 2023 AppleGrowthVision Detection + growth-stage classification 11,397 groups, 21,407 images Stereo RGB + handheld RGB CC BY 4.0 2025 GrowliFlower Instance segmentation + growth-trait regression 24,866 samples, 23,875 groups UAV RGB + multispectral Unspecified 2022 TomatoMAP Classification, detection, instance segmentation 68,069 images RGB (rig + macro) CC BY 4.0 2026 PlantWild Image classification (multimodal) 30,030 images, 146 classes RGB + text CC BY-NC-ND 4.0 2024 Pheno4D Semantic + instance segmentation, registration 223 scans (14 plants) 3D point cloud (laser scan) Unspecified 2021 Crops3D Classification, instance/organ segmentation 1,180 point clouds, 8 crops 3D point cloud (MVS + structured light) CC BY 4.0 2024 VineLiDAR 3D reconstruction (unlabeled) 10 point clouds UAV LiDAR (RGB-colored) CC BY 4.0 2023 Fields of the World Instance + semantic segmentation 70,484 chips, 140,968 samples, 25 countries Sentinel-2 satellite (multispectral, 2-date) Varies by country 2024 AgroMind VQA benchmark (13 task types) 21,339 images, 28,482 QA pairs RGB (satellite/UAV/ground) + text CC BY-SA 4.0 2025 LettuceMOTS Multi-object tracking + segmentation 12 videos, 1,308 frames, 314 tracks RGB video (robot-mounted) CC BY 4.0 2024

Comparison of 14 open agriculture computer vision datasets by task, size, sensor modality, license, and publication year, each linked to its FiftyOne mirror on Hugging Face.

Keep it honest. “Year” above is publication year of the associated paper/release, not necessarily when data collection happened - several of these (WE3DS, PhenoBench) were captured a year or two before publication.

And “Size” reflects each FiftyOne parsing specifically; a couple of the source releases (GrowliFlower, WeedsGalore) ship additional unlabeled orthomosaic imagery that isn’t included in the sample counts above.

Dataset Schema Reference: Media Types, Label Fields & Hugging Face Links

This is the table to bookmark if you already know your sensor and task and just need to know what’s in the box before you load_from_hub(). “Media Type” is the FiftyOne media_type of the dataset (and whether it’s grouped, meaning multiple slices per sample); “Label Fields” lists the actual field names and their FiftyOne label types.

Dataset Media Type Label Fields (FiftyOne Types) CropAndWeed Image ground_truth (Detections, 100 classes), stem_points (Keypoints), segmentation (Segmentation) WeedsGalore Image ground_truth_semantic (Segmentation, 6 classes), ground_truth (Detections, instance masks) PhenoBench Image ground_truth_semantic (Segmentation, 5 classes), ground_truth_plants (Detections), ground_truth_leaves (Detections) WE3DS Image ground_truth (Segmentation, 19 classes), depth_raw/depth_refined (Heatmap) AppleGrowthVision Image, grouped (left/right/image) ground_truth (Detections, 1 class: apple) GrowliFlower Image, grouped (pre/post) plant_instances/leaf_instances (Detections), stem_keypoints (Keypoints), void_mask (Segmentation) TomatoMAP Image, grouped (pos_1-pos_4/macro) ground_truth (Detections, ROI boxes or fruit/flower instance masks), classification (Classification, BBCH stage) PlantWild Image ground_truth (Classification, 146 classes) Pheno4D 3D point cloud ground_truth/ground_truth_collar/ground_truth_tip (Detections, 3D boxes) Crops3D 3D point cloud ground_truth (Classification, 8 crop types) VineLiDAR 3D point cloud None (unlabeled raw geometry) Fields of the World Image, grouped (window_a/window_b) ground_truth_2class/ground_truth_3class (Segmentation), field_instances (Detections) AgroMind Image oc_label/disease/climate_zone (Classification), anomalies (Classifications, multilabel), cultivated_area (Detection), anomaly_at_point (Keypoint) LettuceMOTS Video ground_truth (Detections, per-frame, with mask + tracking index)

FiftyOne media type and label field schema for each of the 14 agriculture datasets, with direct Hugging Face repository links.

Crop and Weed Segmentation Datasets (UAV, RGB-D, and Multispectral)

This is the classic agricultural CV problem: a camera looks straight down at a bed of plants, and the model has to decide, pixel by pixel, which ones are the crop and which ones are stealing its water.

If you’re searching for a crop/weed segmentation dataset, weed detection dataset, or field-scale semantic segmentation dataset, start here.

CropAndWeed CropAndWeed dataset in the FiftyOne app showing top-down field images with crop and weed detection labels CropAndWeed is a crop and weed object detection, semantic segmentation, and stem-keypoint dataset built from 8,034 top-down, real-world field images (74 crop and weed species, 100 fine-grained classes) shot with a fixed 50mm lens at ~1.1m height across four growing seasons in Austria.

Every one of the ~112K plant instances gets a bounding box, a stem keypoint, and a semantic mask, plus per-image moisture / soil / lighting / separability tags describing the conditions it was shot in.

It’s the largest multi-modal (box + point + mask) crop/weed dataset in this list, but the license is non-commercial only - read it before you build a product on it.

Fields. Media type: image (flat). Labels: ground_truth (Detections, box + label_id + stem_x / stem_y), stem_points (Keypoints), segmentation (Segmentation, referencing mask_path). Metadata: recording_set, session, image_id, moisture / soil / lighting / separability (+ numeric _id variants). View CropAndWeed on Hugging Face → WeedsGalore WeedsGalore UAV multispectral maize tiles displayed as a grid in the FiftyOne app WeedsGalore is a UAV-based multispectral crop-and-weed segmentation dataset for maize fields.

156 tiles sound small next to CropAndWeed’s 8,034, but the plant density per image is the highest of any public weed-segmentation dataset at publication time: ~78 annotated instances per 600×600px tile.

The five-band UAV capture (R/G/B/red-edge/NIR) over one real maize field in Potsdam, Germany, across four growth-stage flights, lets you test whether NIR/red-edge actually helps maize vs. amaranth vs. barnyard_grass segmentation over RGB alone.

Fields. Media type: image (flat). Labels: ground_truth_semantic (Segmentation, 6 classes: background / maize / amaranth / barnyard_grass / quickweed / weed_other), ground_truth (Detections, per-instance mask + plant_instance_id). Metadata: ndvi (Heatmap), date, tile_id, tags (train / val / test). View WeedsGalore on Hugging Face → PhenoBench PhenoBench sugar beet crop and weed segmentation samples in the FiftyOne app PhenoBench is a UAV RGB dataset for sugar beet crop and weed semantic, instance, and panoptic segmentation: 2,872 images of a single ~1,300 m² field, annotated at three levels simultaneously - semantic (crop / weed / partial_crop / partial_weed), plant-instance, and, uniquely, crop leaf-instance, with a per-instance visibility score baked into every mask.

That third layer supports “hierarchical panoptic segmentation,” a task designed to mirror how a plant scientist actually counts leaves per plant to gauge growth stage.

Fields. Media type: image (flat). Labels: ground_truth_semantic (Segmentation, 5 classes), ground_truth_plants (Detections, with visibility and mask), ground_truth_leaves (Detections, with visibility and source_plant_instance_id). Metadata: tags (train / val / test), acquisition_date.

Good to know. Why is the test split empty? PhenoBench’s test labels are withheld for a server-side leaderboard at phenobench.org - you submit predictions rather than eval locally. View PhenoBench on Hugging Face → WE3DS WE3DS RGB-D sample with a depth map and per-pixel crop and weed mask in the FiftyOne app WE3DS is an RGB-D (depth) semantic segmentation dataset for crop and weed species identification: 2,568 image pairs from a custom stereo trolley pushed down crop rows at BOKU’s experimental farm in Austria, across 25 measurement dates.

18 real classes (7 crops, 10 weeds, plus soil) get dense per-pixel masks, and every image is paired with a raw and a hole-filled depth map - the paper’s own benchmark shows RGB-D beats RGB-only (70.7% vs. 70.1% mIoU), a small but real signal that depth is worth carrying into your pipeline.

Fields. Media type: image (flat). Labels: ground_truth (Segmentation, 19 classes). Also: depth_raw / depth_refined (Heatmap), location (GeoLocation), weather, wind, height_mm. View WE3DS on Hugging Face →

Orchard, Fruit Detection, and Growth-Stage Phenotyping Datasets

Here, the question isn’t just “what plant is this?”; it’s “what stage of its life cycle is this plant in, and how much fruit will it produce?”

If you’re searching for a fruit detection dataset, BBCH growth-stage classification dataset, or plant phenotyping dataset, this is your group.

AppleGrowthVision AppleGrowthVision orchard images grouped by stereo left, right, and image slices in the FiftyOne app AppleGrowthVision is an apple orchard object detection and BBCH growth-stage classification dataset spanning 11,397 groups (21,407 images) across two very different capture setups: a calibrated stereo rig photographing 33 Jonagold trees in Brandenburg, Germany, over 18 sessions, and handheld smartphone shots of an orchard in Saxony.

Every image is tagged to a specific point on the BBCH phenological scale, and a 6.8% annotated subset carries apple bounding boxes - small but concentrated exactly in the fruit-visible growth stages (BBCH 5-9) where detection actually matters.

Fields. Media type: image, grouped (left / right / image slices). Labels: ground_truth (Detections, single class apple). Metadata: group (Group), bbch_code / bbch_principal_stage / bbch_stage_name, num_apples, reference_apple_count.

Keep it honest. Only ~1,457 of the 21,407 images have ground_truth boxes. Filter to the annotated or has-apples saved views before treating this as a detection-ready set. View AppleGrowthVision on Hugging Face → GrowliFlower GrowliFlower UAV cauliflower plants with colored instance segmentation masks in the FiftyOne app GrowliFlower is a UAV cauliflower instance segmentation and growth-trait pheno