A Deep Learning‐based Computer Vision Approach for Comparative Monarch Butterfly Phenotype Identification in Citizen Science

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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Research on computer vision enhancement in intelligent robot based on machine learning and deep learning

Neural Computing and Applications ◽

10.1007/s00521-021-05898-8 ◽

2021 ◽

Author(s):

Yuhan Ding ◽

Lisha Hua ◽

Shunlei Li

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Intelligent Robot

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An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽

10.3390/s21020343 ◽

2021 ◽

Vol 21 (2) ◽

pp. 343

Author(s):

Kim Bjerge ◽

Jakob Bonde Nielsen ◽

Martin Videbæk Sepstrup ◽

Flemming Helsing-Nielsen ◽

Toke Thomas Høye

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Vision System ◽

Low Cost ◽

Light Trap ◽

Automatic Monitoring ◽

Light Sources ◽

Monitoring Methods ◽

Computer Vision System ◽

Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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A comprehensive review on soil classification using deep learning and computer vision techniques

Multimedia Tools and Applications ◽

10.1007/s11042-021-10544-5 ◽

2021 ◽

Author(s):

Pallavi Srivastava ◽

Aasheesh Shukla ◽

Atul Bansal

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Soil Classification ◽

Comprehensive Review

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A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽

10.3390/drones5010006 ◽

2021 ◽

Vol 5 (1) ◽

pp. 6

Author(s):

Apostolos Papakonstantinou ◽

Marios Batsaris ◽

Spyros Spondylidis ◽

Konstantinos Topouzelis

Keyword(s):

Deep Learning ◽

Data Acquisition ◽

Citizen Science ◽

Coastal Zone ◽

Automatic Detection ◽

Monitoring Methods ◽

Monitoring Method ◽

Marine Litter ◽

Density Maps ◽

Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

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