Plant Classification and Position Estimation for Autonomous Field Robots

2020 ◽  
Author(s):  
Sebastian Haug
Keyword(s):  
Real World ◽  
Precision Agriculture ◽  
Image Data ◽  
Position Estimation ◽  
Single Plant ◽  
Field Robots ◽  
Weed Plants ◽  
Generic Problem ◽  
Vegetable Farming ◽  
Leaf Segmentation

This work presents new approaches to plant classifcation and plant position estimation to enable feld robot based precision agriculture. The developed methods are designed for challenging real world feld situations with small crop plants, presence of close-to-crop weed and overlap of plants. The plant classifcation system is able to distinguish two or more plant classes in feld images without the need for error-prone plant or leaf segmentation. The plant position estimation pipeline solves the generic problem of determining the position of both crop and weed plants only from image data. The combination of both methods allows feld robots to autonomously determine the type and position of plants in the feld to realize precision agriculture tasks such as single plant weed control. Experiments with a feld robot prove the applicability of the presented methods for challenging feld scenarios encountered for example in organic vegetable farming. Contents Symbols and Abbreviations  . . . . . ...

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

scholarly journals Towards an Open Software Platform for Field Robots in Precision Agriculture

Robotics ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 207-234 ◽  
Author(s):  
Kjeld Jensen ◽  
Morten Larsen ◽  
Søren Nielsen ◽  
Leon Larsen ◽  
Kent Olsen ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Software Platform ◽  
Field Robots

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

A Multi-Stage Framework for Classification of Unconstrained Image Data From Mobile Phones

2018 ◽  
pp. 2387-2401
Author(s):  
Shashank Mujumdar ◽  
Dror Porat ◽  
Nithya Rajamani ◽  
L.V. Subramaniam
Keyword(s):  
Image Classification ◽  
Mobile Phones ◽  
Real World ◽  
Image Data ◽  
Complex Environments ◽  
Second Stage ◽  
Classification Framework ◽  
Multi Stage ◽  
Efficient Detection

During the past decade, the number of mobile electronic devices equipped with cameras has increased dramatically and so has the number of real-world applications for image classification. In many of these applications, the image data is captured in an uncontrolled manner and in complex environments and conditions under which existing image classification techniques may not perform well. In this paper, the authors provide a detailed description of an efficient multi-stage image classification framework that is robust enough to remain effective also under challenging imaging conditions, and demonstrate its effectiveness in the context of classification of real-world images of dumpsters captured by mobile phones in the metropolitan city of Hyderabad. Their system is able to achieve accurate classification of the cleanliness state of the dumpsters by utilizing a multi-stage approach, where the first stage is the efficient detection of the dumpster and the second stage is the classification of its state. The authors provide a detailed analysis of the performance of the system as well as comprehensive experimental results on real-world image data.

scholarly journals Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

2020 ◽  
Vol 88 (5) ◽  
pp. 407-422 ◽  
Author(s):  
Ulrike Lussem ◽  
Jürgen Schellberg ◽  
Georg Bareth
Keyword(s):  
Plant Community ◽  
Precision Agriculture ◽  
Imaging System ◽  
Low Cost ◽  
Block Design ◽  
Image Data ◽  
Grassland Management ◽  
Forage Yield ◽  
Management Regime ◽  
Aerial Imaging

Abstract Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

Methods and Applications for Segmenting 3D Medical Image Data

2011 ◽  
pp. 303-320
Author(s):  
Hong Shen
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
General Problem ◽  
Real World ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Segmentation ◽  
Knowledge Based ◽  
Typical Sample ◽  
Medical Image Data

In this chapter, we will give an intuitive introduction to the general problem of 3D medical image segmentation. We will give an overview of the popular and relevant methods that may be applicable, with a discussion about their advantages and limits. Specifically, we will discuss the issue of incorporating prior knowledge into the segmentation of anatomic structures and describe in detail the concept and issues of knowledge-based segmentation. Typical sample applications will accompany the discussions throughout this chapter. We hope this will help an application developer to improve insights in the understanding and application of various computer vision approaches to solve real-world problems of medical image segmentation.

scholarly journals Potential Non-Invasive Technique for Accessing Plant Water Contents Using a Radar System

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 279
Author(s):  
Luís Carlos Santos ◽  
Filipe Neves dos Santos ◽  
Raul Morais ◽  
Cândido Duarte
Keyword(s):  
Precision Agriculture ◽  
Sap Flow ◽  
Sensor Technology ◽  
Invasive Technique ◽  
Single Plant ◽  
Flow Measurements ◽  
Non Invasive ◽  
Rf Antennas ◽  
Rf Signals ◽  
Sap Flow Measurements

Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution—for the automotive industry—to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.

scholarly journals Precision Vegetable Farming Technologies: An Update

2021 ◽  
Author(s):  
Prashant Kaushik
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Economic Benefits ◽  
Geographical Information ◽  
Small Scale ◽  
Positioning System ◽  
Vegetable Crops ◽  
Vegetable Farming

The efficiency of precision vegetable farming cannot deny in the current of climate change. As compared to west adoption of reliability precision agriculture approaches in developing world is a gradual procedure. The small scale of farms limits economic benefits from currently accessible precision farming technologies. Nevertheless, horticulture interventions like geographical positioning system (GPS), geographical information system (GIS), artificial intelligence (AI), robotics, sensor technologies, etc., are being utilized for precision vegetable farming to improve production and quality of vegetables. This retains excellent promise for developing vegetable crops within the present farming scenario when climate change makes the whole rethink agriculture practices. Overall, this chapter will provide useful information about precision vegetable farming technologies for vegetable growers, enthusiasts, farmers, and researchers.

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