Query: machine vision

Candidate papers:

abstract: Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame based camera, converted into a digital format and processed afterwards using a machine learning algorithm such as an artificial neural network (ANN) 1 The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed 2 7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two dimensional (2D) semiconductor 8 9 photodiode 10 12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second. A two dimensional semiconductor photodiode array senses and processes optical images simultaneously without latency, and is trained to classify and encode images with high throughput, acting as an artificial neural network.
author_names: Lukas Mennel

Joanna Karolina Symonowicz

Stefan Wachter

Dmitry K Polyushkin

Aday J Molina-Mendoza

T Mueller
title: Ultrafast machine vision with 2D material neural network image sensors
venue: Nature
year: 2020

abstract: Abstract Over the years little research has been performed for vision based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision based technology we find a good solution to this problem. Papaya disease recognition mainly involves two challenging problems: one is disease detection and another is disease classification. Considering this scenario, here we present an online machine vision based agro medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem. Some experiments are performed to show the utility of the proposed expert system. First, we propose a set of features from the view point of distinguishing attributes. K means clustering algorithm is used in order to segment out the disease attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine. More than 90% classification accuracy has been achieved, which appears to be good as well as promising by comparing performances obtained with recently reported relevant works.
author_names: Md Tarek Habib

Anup Majumder

Atm Jakaria

Morium Akter

Mohammad Shorif Uddin

Farruk Ahmed
title: Machine vision based papaya disease recognition
venue: J. King Saud Univ. Comput. Inf. Sci.
year: 2020

abstract: An image sensor array has been developed that acts as its own artificial neural network to capture and identify optical images simultaneously, processing the information rapidly without needing to convert it to a digital format. An array of photosensors that acts as an artificial neural network.
author_names: Yang Chai
title: In sensor computing for machine vision
venue: Nature
year: 2020

abstract: Abstract With being rapid increasing population in worldwide, the need for satisfactory level of crop production with decreased amount of agricultural lands. Machine vision would ensure the increase of crop production by using an automated, non destructive and cost effective technique. In last few years, remarkable results have been achieved in different sectors of agriculture. These achievements are integrated with machine learning techniques on machine vision approach that cope with colour, shape, texture and spectral analysis from the image of objects. Despite having many applications of different machine learning techniques, this review only described the statistical machine learning technologies with machine vision systems in agriculture due to broad area of machine learning applications. Two types of statistical machine learning techniques such as supervised and unsupervised learning have been utilized for agriculture. This paper comprehensively surveyed current application of statistical machine learning techniques in machine vision systems, analyses each technique potential for specific application and represents an overview of instructive examples in different agricultural areas. Suggestions of specific statistical machine learning technique for specific purpose and limitations of each technique are also given. Future trends of statistical machine learning technology applications are discussed.
author_names: Tanzeel Ur Rehman

Md Sultan Mahmud

Young K Chang

Jian Jin

Jaemyung Shin
title: Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
venue: Comput. Electron. Agric.
year: 2019

abstract: A new technique for three dimensional (3D) camera calibration for machine vision metrology using off the shelf TV cameras and lenses is described. The two stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two stage calibration can be done in real time.
author_names: Roger Y Tsai
title: A versatile camera calibration technique for high accuracy 3D machine vision metrology using off the shelf TV cameras and lenses
venue: IEEE J. Robotics Autom.
year: 1987

abstract: Abstract Weeds are among the major factors that could harm crop yield. With the advances in electronic and information technologies, machine vision combined with image processing techniques has become a promising tool for precise real time weed and crop detection in the field, providing valuable sensing information for site specific weed management. This review summarized the advances of weed detection using ground based machine vision and image processing techniques. Concretely, the four procedures, i.e. pre processing, segmentation, feature extraction and classification, for weed detection were presented in detail. To separate vegetation from background, different color indices and classification approaches like color index based, threshold based and learning based ones, were developed. The difficulty of weed detection lies in discriminating between crops and weeds that often have similar properties. Generally, four categories of features, i.e. biological morphology, spectral features, visual textures and spatial contexts, were used for the task, which were discussed in this review. Application of conventional machine learning based and recently developed deep learning based approaches for weed detection were also presented. Finally, challenges and solutions provided by researchers for weed detection in the field, including occlusion and overlap of leaves, varying lighting conditions and different growth stages, were discussed.
author_names: Aichen Wang

Wen Zhang

Xinhua Wei
title: A review on weed detection using ground based machine vision and image processing techniques
venue: Comput. Electron. Agric.
year: 2019

abstract: 1 Introduction. 2 The digitized image and its properties. 3 Data structures for image analysis. 4 Image pre processing. 5 Segmentation. 6 Shape representation and description. 7 Object recognition. 8 Image understanding. 9 3D Vision. 10 Mathematical morphology. 11 Linear discrete image transforms. 12 Image data compression. 13 Texture. 14 Motion analysis.
author_names: Milan Sonka

Vaclav Hlavac

Roger D Boyle
title: Image Processing, Analysis and Machine Vision
venue: Springer US
year: 1993

abstract: Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer, and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana (L. and the monocot barley (Hordeum vulgare L. plants under their diurnal light/dark cycles.
author_names: Ayan Chaudhury

Christopher D W Ward

Ali Talasaz

Alexander G Ivanov

Mark Brophy

Bernard Grodzinski

Norman P A Huner

Rajnikant V Patel

John L Barron
title: Machine Vision System for 3D Plant Phenotyping
venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics
year: 2019

abstract: Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.
author_names: Efthimia Mavridou

Eleni Vrochidou

George A Papakostas

Theodore P Pachidis

Vassilis G Kaburlasos
title: Machine Vision Systems in Precision Agriculture for Crop Farming
venue: J. Imaging
year: 2019

abstract: Significance Plant stress identification based on visual symptoms has predominately remained a manual exercise performed by trained pathologists, primarily due to the occurrence of confounding symptoms. However, the manual rating process is tedious, is time consuming, and suffers from inter and intrarater variabilities. Our work resolves such issues via the concept of explainable deep machine learning to automate the process of plant stress identification, classification, and quantification. We construct a very accurate model that can not only deliver trained pathologist level performance but can also explain which visual symptoms are used to make predictions. We demonstrate that our method is applicable to a large variety of biotic and abiotic stresses and is transferable to other imaging conditions and plants. Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar stresses in soybean [Glycine max (L. Merr. with remarkable accuracy. We also present an explanation mechanism, using the top K high resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress) classification (low, medium, or high stress) and quantification (stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar stresses would have significant implications in scientific research, plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g. unmanned air vehicles and automated ground scouts) for rapid, large scale scouting or as a mobile application for real time detection of stress by farmers and researchers.
author_names: Sambuddha Ghosal

David Blystone

Asheesh K Singh

Baskar Ganapathysubramanian

Arti Singh

Soumik Sarkar
title: An explainable deep machine vision framework for plant stress phenotyping
venue: Proceedings of the National Academy of Sciences
year: 2018