Deep Transfer Learning For Plant Center Localization

Cai, Enyu; Baireddy, Sriram; Yang, Clifford; Crawford, Melba M.; Delp, Edward J.

doi:10.1109/cvprw50498.2020.00039

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…Transfer learning strategies have also been used in agricultural studies (Wang and Su, 2022). Abdalla et al (2019) evaluated three transfer learning methods using a VGG-based encoder net for oilseed rapes image segmentation; Cai et al (2020) proposed a modified U-Net architecture with transfer learning strategy for detecting plant locations; utilized transfer learning with Mask R-CNN for soybean seed segmentation. For the RNN architecture, Wang et al (2018) proposed a deep transfer learning framework with LSTM-based RNN for soybean yield prediction in different countries with satellite imagery.…”

Section: Transfer Learningmentioning

confidence: 99%

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

2023

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Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

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Section: Transfer Learningmentioning

confidence: 99%

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

2023

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“…This work proved that TL could provide important benefits for automated plant identification. In addition, TL was successfully utilized to locate plant centers with limited ground truth data by Cai, E. et al (2020). They also concluded that TL was not effective if the distribution of the source domain was significantly divergent from the target domain [105].…”

Section: Transfer Learning (Tl) In Pamentioning

confidence: 99%

“…In addition, TL was successfully utilized to locate plant centers with limited ground truth data by Cai, E. et al (2020). They also concluded that TL was not effective if the distribution of the source domain was significantly divergent from the target domain [105]. Many well-known pretrained models, such as AlexNet, VGGNet, and GoogLeNet, can be incorporated into TL as backbones.…”

Section: Transfer Learning (Tl) In Pamentioning

confidence: 99%

“…(iii). Some novel strategies, such as transfer learning [104][105][106][107][108][109], few-shot learning [31,32,114,115], graph convolutional networks (GCN) [66], generative adversarial networks (GAN) [127], and semi-supervised learning [128] have been proposed to reduce the dependency of DL models on the agricultural datasets. However, their performance has not been fully released.…”

Section: Deep Learning Algorithms and Neural Networkmentioning

confidence: 99%

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A Review of Deep Learning in Multiscale Agricultural Sensing

et al. 2022

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Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.

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“…Those, however, showed limitations, such as performing worse on larger objects or objects distant from each other. This topic is generally little explored [25][26][27]. Instead of using an algorithm that directly takes point labels as input, another option could be converting the point labels to bounding boxes first, which then are used to train an object detection algorithm requiring bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data

et al. 2023

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Monitoring tree diseases in forests is crucial for managing pathogens, particularly as climate change and globalization lead to the emergence and spread of tree diseases. Object detection algorithms for monitoring tree diseases through remote sensing rely on bounding boxes to represent trees. However, this approach may not be the most efficient. Our study proposed a solution to this challenge by applying object detection to unmanned aerial vehicle (UAV)-based imagery, using point labels that were converted into equally sized square bounding boxes. This allowed for effective and extensive monitoring of black pine (Pinus nigra L.) trees with vitality-related damages. To achieve this, we used the “You Only Look Once’’ version 5 (YOLOv5) deep learning algorithm for object detection, alongside a 16 by 16 intersection over union (IOU) and confidence threshold grid search, and five-fold cross-validation. Our dataset used for training and evaluating the YOLOv5 models consisted of 179 images, containing a total of 2374 labeled trees. Our experiments revealed that, for achieving the best results, the constant bounding box size should cover at least the center half of the tree canopy. Moreover, we found that YOLOv5s was the optimal model architecture. Our final model achieved competitive results for detecting damaged black pines, with a 95% confidence interval of the F1 score of 67–77%. These results can possibly be improved by incorporating more data, which is less effort-intensive due to the use of point labels. Additionally, there is potential for advancements in the method of converting points to bounding boxes by utilizing more sophisticated algorithms, providing an opportunity for further research. Overall, this study presents an efficient method for monitoring forest health at the single tree level, using point labels on UAV-based imagery with a deep learning object detection algorithm.

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Deep Transfer Learning For Plant Center Localization

Cited by 13 publications

References 24 publications

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

A Review of Deep Learning in Multiscale Agricultural Sensing

Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data

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