# Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

arXiv: https://arxiv.org/abs/1612.03019 · HTML: https://mlsanity.com/paper/1612.03019/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2016.

## Built on by (most cited first, 14 of 14)

- [AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning](https://mlsanity.com/paper/2410.08405/) (2024)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Towards practical object detection for weed spraying in precision agriculture](https://mlsanity.com/paper/2109.11048/) (2021)
- [Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation](https://mlsanity.com/paper/2106.04208/) (2021)
- [Tomato Maturity Recognition with Convolutional Transformers](https://mlsanity.com/paper/2307.01530/) (2023)
- [Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves](https://mlsanity.com/paper/2310.06582/) (2023)
- [Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2407.14119/) (2024)
- [Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks](https://mlsanity.com/paper/2102.01460/) (2021)
- [Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation](https://mlsanity.com/paper/2109.07247/) (2021)
- [Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves](https://mlsanity.com/paper/2501.00527/) (2024)
- [IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping](https://mlsanity.com/paper/2505.08273/) (2025)
- [Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing](https://mlsanity.com/paper/2505.08302/) (2025)
- [CropCraft: A Procedural World Generator for Robotic Simulation of Agricultural Tasks](https://mlsanity.com/paper/2511.02417/) (2025)

## Abstract (arXiv)

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.