Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

Article Computer Science, Artificial Intelligence

A detection algorithm for cherry fruits based on the improved YOLO-v4 model

Rongli Gai et al.

Summary: Digital agriculture has a significant impact on the value of agricultural output. Robotic picking and cherry fruit detection technologies enable more intelligent and productive farming practices.

NEURAL COMPUTING & APPLICATIONS (2023)

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Article Geochemistry & Geophysics

Contrasting YOLOv5, Transformer, and EfficientDet Detectors for Crop Circle Detection in Desert

Mohamed Lamine Mekhalfi et al.

Summary: The ongoing discoveries of water reserves have led to the increasing use of crop circles in desert areas in several countries. This study compares the effectiveness of three deep learning models, Detection Transformers, EfficientDet, and YOLOv5, for detecting and counting crop circles in remote desert regions.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Engineering, Electrical & Electronic

Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4

Zakria et al.

Summary: In this article, a deep-learning-based target detection model is chosen to improve the accuracy and robustness of target detection in remote sensing images. By improving the YOLOv4 network and proposing new approaches, the accuracy and detection performance have been enhanced.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

Hao Lu et al.

Summary: Fast and accurate plant counting tools have revolutionized modern agriculture. However, agricultural practitioners require the output of these tools to be not only accurate but also explainable. To improve the poor explainability of output visualization, researchers propose the idea of guided upsampling and background suppression, which are integrated into the TasselNetV3 counting model. Experimental results on multiple datasets show that guided upsampling and background suppression not only improve counting performance but also enable explainable visualization.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Engineering, Environmental

Deep learning-based waste detection in natural and urban environments

Sylwia Majchrowska et al.

Summary: This article addresses the problems in waste detection research by analyzing waste datasets and deep learning methods, providing benchmarks and standards, proposing new benchmark datasets, and presenting a two stage detector for litter localization and classification. The proposed approach achieves high precision in waste detection and classification accuracy on the test dataset.

WASTE MANAGEMENT (2022)

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Article Environmental Sciences

Deep Convolutional Neural Network for Rice Density Prescription Map at Ripening Stage Using Unmanned Aerial Vehicle-Based Remotely Sensed Images

Lele Wei et al.

Summary: In this paper, UAV and YOLOv4 deep neural network were used to detect mature rice ears and generate rice density prescription maps. The study showed that the CNN model had excellent robustness and accuracy in detecting rice in different health states.

REMOTE SENSING (2022)

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Article Agronomy

The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4

Chengliang Zhang et al.

Summary: This study developed an impurity detection system for seed cotton, which achieved segmentation, classification, and recognition of machine-picked seed cotton images using a multi-channel fusion segmentation algorithm and an improved YOLOv4 network model. The experimental results showed that the system effectively obtained the impurity content in machine-picked cotton and increased the detection accuracy of the impurity rate.

AGRONOMY-BASEL (2022)

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Article Computer Science, Artificial Intelligence

Towards improved accuracy of UAV-based wheat ears counting: A transfer learning method of the ground-based fully convolutional network

Juncheng Ma et al.

Summary: A transfer learning method using EarDensityNet was proposed for accurate UAV-based wheat ear counting, showing better performance compared to training from scratch. The study demonstrated the potential of transfer learning for improving accuracy and applicability in agricultural applications.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Agriculture, Multidisciplinary

Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry

Abel Barreto et al.

Summary: This study demonstrated the feasibility of automatic crop counting using UAVs carrying RGB cameras and deep learning methods. The results showed that the FCN pipeline could accurately count crop numbers, with forecast errors lower than 4.6% for sugar beet fields. Additionally, the predictions for maize and strawberry fields also had errors below 4% under practical field conditions.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Agriculture, Multidisciplinary

Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping

Norman Wilke et al.

Summary: This study demonstrated high-throughput phenotyping of cereal plant density using UAV image data, with developed empirical regression models showing high prediction accuracy and robustness. Evaluation on spring barley and spring wheat experiments, as well as 42 winter wheat genotypes, confirmed the practicality and repeatability of the approach.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

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Article Chemistry, Analytical

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Le Wang et al.

Summary: In this research project, an automated method for corn stand counting based on color images extracted from video clips was developed using the YoloV3 network and Kalman filter. The system achieved an accuracy of over 98% at growth stages V2 and V3 with an average frame rate of 47 FPS, offering a robust and high-throughput solution for corn breeding and plant genetic research.

SENSORS (2021)

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Article Geography, Physical

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery

Lucas Prado Osco et al.

Summary: Accurately mapping croplands is crucial for precision farming, and optical imaging with sensors mounted on UAVs is a cost-effective solution. Developing an architecture capable of simultaneously extracting plants and plantation-rows from UAV images is important for supporting agricultural system management.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

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Article Agronomy

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

Yan Zhao et al.

Summary: A pipeline was developed to extract reflectance data from UAV images for high-throughput phenotyping, showing accurate estimation of plant number and sorghum head count using single nadir images. The reflectance information obtained demonstrated high discriminative ability for sorghum head colors, allowing accurate segmentation of crop organs at the canopy level.

PLANT PHENOMICS (2021)

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Article Environmental Sciences

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Jiahuan Jiang et al.

Summary: The paper proposes a multi-channel fusion SAR image processing method based on the latest YOLO-V4-light deep learning framework, achieving efficient ship detection and delivering good results in experiments.

REMOTE SENSING (2021)

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Article Agriculture, Multidisciplinary

Early corn stand count of different cropping systems using UAV-imagery and deep learning

Chin Nee Vong et al.

Summary: This study evaluated the feasibility of using a UAV-based imaging system for estimating early corn stand count in different cropping systems. Results showed that accuracy for stand count estimation was influenced by the cropping systems, with the DL model performing best in the MTCS system.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Environmental Sciences

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Pengfei Shi et al.

Summary: This study addresses the challenging task of detecting small objects like oil wells in large-scale and high-resolution remote sensing images. The improved algorithm based on YOLO v4 effectively solves the problems of repeated detection and inaccurate positioning, significantly increasing the accuracy of detection results.

REMOTE SENSING (2021)

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Article Agronomy

Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

Adel Ammar et al.

Summary: This paper proposes an original deep learning framework for automated counting and geolocation of palm trees from aerial images using convolutional neural networks. YOLOv4 and EfficientDet-D5 models achieved the best trade-off between accuracy and speed. Geotagged metadata and photogrammetry concepts were utilized for automatic geolocation of detected palm trees.

AGRONOMY-BASEL (2021)

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Article Agronomy

Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution

K. Velumani et al.

Summary: Early-stage plant density is a crucial trait for determining the fate of a genotype in specific environmental conditions and management practices. The use of high-resolution RGB images from UAVs can significantly improve plant detection and counting performance, especially when trained on high-resolution images. Additionally, training on a mixture of high- and low-resolution images can lead to good performances on both types of images.

PLANT PHENOMICS (2021)

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Proceedings Paper Automation & Control Systems

A Fusion Framework for Multi-Spectral Pedestrian Detection using EfficientDet

Jongchan Kim et al.

Summary: Research on Advanced Driver Assistance Systems is currently active. The paper constructs an EfficientDet Fusion Framework for multi-spectral pedestrian detection through combining Sum, Max, and Concatenation at the feature level, showing a 10% performance improvement compared to single spectral networks. The study indicates that the shortcomings of a single spectral network can be compensated through the resulting image. Future research will continue to explore various fusion studies based on the EfficientDet Fusion Framework.

2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021) (2021)

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Article Engineering, Electrical & Electronic

Multilayer Feature Extraction Network for Military Ship Detection From High-Resolution Optical Remote Sensing Images

Peng Qin et al.

Summary: This study proposed a method for classifying maritime military targets using high-resolution optical remote sensing images, which utilizes feature extraction networks, attention mechanisms, and multiclassification methods to address sample scarcity and imbalance issues, ultimately achieving accurate identification of multiple types of military ships.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Article Computer Science, Artificial Intelligence

Scale specified single shot multibox detector

Xiaoqiang Li et al.

IET COMPUTER VISION (2020)

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Active learning with point supervision for cost-effective panicle detection in cereal crops

Akshay L. Chandra et al.

PLANT METHODS (2020)

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Article Environmental Sciences

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Yunling Liu et al.

REMOTE SENSING (2020)

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Article Plant Sciences

Automatic wheat ear counting using machine learning based on RGB UAV imagery

Jose A. Fernandez-Gallego et al.

PLANT JOURNAL (2020)

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Article Environmental Sciences

Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms

Aijing Feng et al.

REMOTE SENSING (2020)

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Article Agronomy

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

Hector Garcia-Martinez et al.

AGRONOMY-BASEL (2020)

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Article Agriculture, Multidisciplinary

Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence

Yiannis Ampatzidis et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

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Wheat ear counting using K-means clustering segmentation and convolutional neural network

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PLANT METHODS (2020)

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Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD

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SENSORS (2020)

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Accretion-Erosion Dynamics of the Yellow River Delta and the Relationships with Runoff and Sediment from 1976 to 2018

He Li et al.

WATER (2020)

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Sorghum Panicle Detection and Counting Using Unmanned Aerial System Images and Deep Learning

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Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments

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COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

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Article Agriculture, Multidisciplinary

Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery

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COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

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Review Computer Science, Information Systems

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IEEE ACCESS (2020)

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Apple detection during different growth stages in orchards using the improved YOLO-V3 model

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COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

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Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network

Jintao Wu et al.

REMOTE SENSING (2019)

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ASSD: Attentive single shot multibox detector

Jingru Yi et al.

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DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Pouria Sadeghi-Tehran et al.

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Simon Madec et al.

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A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Lonesome Malambo et al.

REMOTE SENSING (2019)

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Article Agriculture, Multidisciplinary

Seed-per-pod estimation for plant breeding using deep learning

L. C. Uzal et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

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Review Agriculture, Multidisciplinary

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Andreas Kamilaris et al.

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Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination

Joshua Chopin et al.

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An automated, high-throughput plant phenotyping system using machine learning based plant segmentation and image analysis

Unseok Lee et al.

PLOS ONE (2018)

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Aerial Imagery Analysis - Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy

Wei Guo et al.

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Michal Koziarski et al.

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Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

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An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops

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Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

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