633 research outputs found
Apple Flower Detection Using Deep Convolutional Networks
To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability
Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network
In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method
Detection and localization of cotton based on deep neural networks
Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.
 
Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination
Early-stage identification of fruit flowers that are in both opened and
unopened condition in an orchard environment is significant information to
perform crop load management operations such as flower thinning and pollination
using automated and robotic platforms. These operations are important in
tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance
the overall profit. The recent development in agricultural automation suggests
that this can be done using robotics which includes machine vision technology.
In this article, we proposed a vision system that detects early-stage flowers
in an unstructured orchard environment using YOLOv5 object detection algorithm.
For the robotics implementation, the position of a cluster of the flower
blossom is important to navigate the robot and the end effector. The centroid
of individual flowers (both open and unopen) was identified and associated with
flower clusters via K-means clustering. The accuracy of the opened and unopened
flower detection is achieved up to mAP of 81.9% in commercial orchard images
The Use of Agricultural Robots in Orchard Management
Book chapter that summarizes recent research on agricultural robotics in
orchard management, including Robotic pruning, Robotic thinning, Robotic
spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page
Stochastic Methods for Fine-Grained Image Segmentation and Uncertainty Estimation in Computer Vision
In this dissertation, we exploit concepts of probability theory, stochastic methods and machine learning to address three existing limitations of deep learning-based models for image understanding. First, although convolutional neural networks (CNN) have substantially improved the state of the art in image understanding, conventional CNNs provide segmentation masks that poorly adhere to object boundaries, a critical limitation for many potential applications. Second, training deep learning models requires large amounts of carefully selected and annotated data, but large-scale annotation of image segmentation datasets is often prohibitively expensive. And third, conventional deep learning models also lack the capability of uncertainty estimation, which compromises both decision making and model interpretability. To address these limitations, we introduce the Region Growing Refinement (RGR) algorithm, an unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence labels into regions of low-confidence classification. The probabilistic Region Growing Refinement (pRGR) provides RGR with a rigorous mathematical foundation that exploits concepts of Bayesian estimation and variance reduction techniques. Experiments demonstrate both the effectiveness of (p)RGR for the refinement of segmentation predictions, as well as its suitability for uncertainty estimation, since its variance estimates obtained in the Monte Carlo iterations are highly correlated with segmentation accuracy. We also introduce FreeLabel, an intuitive open-source web interface that exploits RGR to allow users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset. The practical relevance of methods developed in this dissertation are illustrated through applications on agricultural and healthcare-related domains. We have combined RGR and modern CNNs for fine segmentation of fruit flowers, motivated by the importance of automated bloom intensity estimation for optimization of fruit orchard management and, possibly, automatizing procedures such as flower thinning and pollination. We also exploited an early version of FreeLabel to annotate novel datasets for segmentation of fruit flowers, which are currently publicly available. Finally, this dissertation also describes works on fine segmentation and gaze estimation for images collected from assisted living environments, with the ultimate goal of assisting geriatricians in evaluating health status of patients in such facilities
Tomato Flower Detection and Three-Dimensional Mapping for Precision Pollination
It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less environmental control. In response to this crisis, this research aims to act as a vital first step towards the development of a more robust autonomous pollination system for agricultural crop production. The main objective of this research is to develop a flower detection and mapping system for a field crop setting. This research presents a method to detect and localize tomato flowers within a three-dimensional (3D) region. Tomato plants were grown in a raised-bed garden where images were collected of the overhead view of the plants. Images were then stitched together using a photogrammetry technique, accomplished by the Pix4Dmapper software, to form an orthomosaic and 3D representation of the raised-bed garden from a high spatial resolution aerial view. Various machine learning architectures were trained to detect tomato flowers from overhead images and were then tested on the orthomosaic images produced by the Pix4D software. The coordinates of the detected flowers in the orthomosaic were then compared to the 3D model representation to find approximate 3D coordinates for each of the flowers relative to a predefined origin. This research serves as a first step in autonomous pollination by presenting a way for machine vision and machine learning to be used to identify the presence and location of flowers on tomato crops. Future work will aim to expand flower detection to other crops varieties in varying field conditions
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
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