# Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach

arXiv: https://arxiv.org/abs/2401.15785 · HTML: https://mlsanity.com/paper/2401.15785/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)

## Built on by (most cited first, 7 of 7)

- [Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies](https://mlsanity.com/paper/2607.02092/) (2026)
- [A Decade of You Only Look Once (YOLO) for Object Detection: A Review](https://mlsanity.com/paper/2504.18586/) (2025)
- [Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming](https://mlsanity.com/paper/2512.15735/) (2025)
- [Efficient Self-Supervised Neuro-Analytic Visual Servoing for Real-time Quadrotor Control](https://mlsanity.com/paper/2507.19878/) (2025)
- [Spatial Transformer Network YOLO Model for Agricultural Object Detection](https://mlsanity.com/paper/2407.21652/) (2024)
- [CBAM-STN-TPS-YOLO: Enhancing Agricultural Object Detection through Spatially Adaptive Attention Mechanisms](https://mlsanity.com/paper/2506.07357/) (2025)
- [SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models](https://mlsanity.com/paper/2606.20857/) (2026)

## Abstract (arXiv)

The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.