# Deep Fruit Detection in Orchards

arXiv: https://arxiv.org/abs/1610.03677 · HTML: https://mlsanity.com/paper/1610.03677/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2016.

## Built on by (most cited first, 30 of 38)

- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection](https://mlsanity.com/paper/2108.13344/) (2021)
- [A fast accurate fine-grain object detection model based on YOLOv4 deep neural network](https://mlsanity.com/paper/2111.00298/) (2021)
- [Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques](https://mlsanity.com/paper/2401.08629/) (2023)
- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models](https://mlsanity.com/paper/2208.02707/) (2022)
- [Apple Counting using Convolutional Neural Networks](https://mlsanity.com/paper/2208.11566/) (2022)
- [Fruit Ripeness Classification: a Survey](https://mlsanity.com/paper/2212.14441/) (2022)
- [AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture](https://mlsanity.com/paper/2309.13393/) (2023)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures](https://mlsanity.com/paper/2105.14572/) (2021)
- [Localizing Small Apples in Complex Apple Orchard Environments](https://mlsanity.com/paper/2202.11372/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [CitDet: A Benchmark Dataset for Citrus Fruit Detection](https://mlsanity.com/paper/2309.05645/) (2023)
- [MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models](https://mlsanity.com/paper/2407.04711/) (2024)
- [In-field high throughput grapevine phenotyping with a consumer-grade depth camera](https://mlsanity.com/paper/2104.06945/) (2021)
- [Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species](https://mlsanity.com/paper/2209.04618/) (2022)
- [S3AD: Semi-supervised Small Apple Detection in Orchard Environments](https://mlsanity.com/paper/2311.05029/) (2023)
- [A methodology for detection and localization of fruits in apples orchards from aerial images](https://mlsanity.com/paper/2110.12331/) (2021)
- [A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management](https://mlsanity.com/paper/2509.20580/) (2025)
- [TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting](https://mlsanity.com/paper/2509.20857/) (2025)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [Deep Learning approach for Classifying Trusses and Runners of Strawberries](https://mlsanity.com/paper/2207.02721/) (2022)
- [Small, but important: Traffic light proposals for detecting small traffic lights and beyond](https://mlsanity.com/paper/2307.15191/) (2023)
- [High-Speed Detector For Low-Powered Devices In Aerial Grasping](https://mlsanity.com/paper/2402.14591/) (2024)
- [Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision](https://mlsanity.com/paper/2407.16102/) (2024)
- [Foundation Model-Based Apple Ripeness and Size Estimation for Selective Harvesting](https://mlsanity.com/paper/2502.01850/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Grounding](https://mlsanity.com/paper/2605.22034/) (2026)
- [Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting](https://mlsanity.com/paper/2105.03832/) (2021)

## Abstract (arXiv)

An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of >0.9 achieved for apples and mangoes.