High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Trevisan, Rodrigo; Pérez, Osvaldo; Schmitz, Nathan; Diers, Brian W.; Martín, Nicolas F.

doi:10.20944/preprints202009.0458.v1

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Multilayer perceptrons (MLPs; fully connected layers) and Convolutional Neural Networks (CNNs; fully connected layers and convolutional/pooling filters) are two common types of neural networks (NN). These methods are characterized by the sequentially stacking (several) layers, which automatically identifies latent patterns or features from data ( Trevisan et al, 2020 ). For a technically accurate and contextualized explanation of such models, refer to Pérez-Enciso and Zingaretti (2019) .…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids

et al. 2022

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Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

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Section: Introductionmentioning

confidence: 99%

Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids

et al. 2022

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show abstract

“…Multilayer perceptrons (MLPs; fully connected layers) and Convolutional Neural Networks (CNNs; fully connected layers and convolutional/pooling filters) are two common types of neural networks. These methods are characterized by the sequentially stacking (several) layers, which automatically identifies latent patterns or features from data (Trevisan et al 2020). For a technically accurate and contextualized explanation of such models, refer to Pérez-Enciso and Zingaretti (2019).…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

Galli

Sabadin

Yassue

et al. 2021

Preprint

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Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

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UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring

Yue

et al. 2023

Agriculture

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Timely and accurate monitoring of fractional vegetation cover (FVC), leaf chlorophyll content (LCC), and maturity of breeding material are essential for breeding companies. This study aimed to estimate LCC and FVC on the basis of remote sensing and to monitor maturity on the basis of LCC and FVC distribution. We collected UAV-RGB images at key growth stages of soybean, namely, the podding (P1), early bulge (P2), peak bulge (P3), and maturity (P4) stages. Firstly, based on the above multi-period data, four regression techniques, namely, partial least squares regression (PLSR), multiple stepwise regression (MSR), random forest regression (RF), and Gaussian process regression (GPR), were used to estimate the LCC and FVC, respectively, and plot the images in combination with vegetation index (VI). Secondly, the LCC images of P3 (non-maturity) were used to detect LCC and FVC anomalies in soybean materials. The method was used to obtain the threshold values for soybean maturity monitoring. Additionally, the mature and immature regions of soybean were monitored at P4 (mature stage) by using the thresholds of P3-LCC. The LCC and FVC anomaly detection method for soybean material presents the image pixels as a histogram and gradually removes the anomalous values from the tails until the distribution approaches a normal distribution. Finally, the P4 mature region (obtained from the previous step) is extracted, and soybean harvest monitoring is carried out in this region using the LCC and FVC anomaly detection method for soybean material based on the P4-FVC image. Among the four regression models, GPR performed best at estimating LCC (R2: 0.84, RMSE: 3.99) and FVC (R2: 0.96, RMSE: 0.08). This process provides a reference for the FVC and LCC estimation of soybean at multiple growth stages; the P3-LCC images in combination with the LCC and FVC anomaly detection methods for soybean material were able to effectively monitor soybean maturation regions (overall accuracy of 0.988, mature accuracy of 0.951, immature accuracy of 0.987). In addition, the LCC thresholds obtained by P3 were also applied to P4 for soybean maturity monitoring (overall accuracy of 0.984, mature accuracy of 0.995, immature accuracy of 0.955); the LCC and FVC anomaly detection method for soybean material enabled accurate monitoring of soybean harvesting areas (overall accuracy of 0.981, mature accuracy of 0.987, harvested accuracy of 0.972). This study provides a new approach and technique for monitoring soybean maturity in breeding fields.

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High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Cited by 10 publications

References 10 publications

Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids

Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring

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