Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings

Picek, Lukáš; Šulc, Milan; Patel, Yash; Matas, Jiřı́

doi:10.3389/fpls.2022.787527

Cited by 20 publications

(7 citation statements)

References 37 publications

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“…Chen et al [42] reviewed the methods of deep learning for plant image recognition in the past five years focusing on the structure of the convolutional neural networks used in plant recognition and classification and the methods of improving convolutional neural networks, as well as the ways of image collection and processing. Picek et al [43] proposed a novel image retrieval method based on nearest neighbor classification, which can be carried out in deep embedded space, providing a new solution for complex recognition tasks. This method can not only improve the accuracy and robustness of classification, but also help the visualization of prediction samples, and achieve good results.…”

Section: Discussion Of Image Data and Deep Learningmentioning

confidence: 99%

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

Liu,

Zhu,

Liu

et al. 2024

Plants

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The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.

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Section: Discussion Of Image Data and Deep Learningmentioning

confidence: 99%

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

Liu,

Zhu,

Liu

et al. 2024

Plants

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“…Deep learning approaches are now quite common for animal and plant species identification, particularly for citizen science projects (Willi et al ., 2019; Picek et al ., 2022), but remain so far very new when it comes to archaeological material (but see Miele et al, 2020) or morphometrics (but see Le et al, 2020). To the best of our knowledge, this is the first time CNN are used for such sub-specific identification task in plants, a fortiori on four different model taxa.…”

Section: Discussionmentioning

confidence: 99%

Deep learningversusgeometric morphometrics for archaeobotanical domestication study and subspecific identification

Bonhomme,

Bouby,

Claude

et al. 2023

Preprint

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Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This 'pre-distilled' geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.

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“…Plant recognition using supervised machine learning can bene t signi cantly from an increased volume of annotated data for training [102][103][104]. However, obtaining high-quality, consistent annotations is a time-consuming and costly process, often requiring a well-de ned multi-stage process involving multiple team members with specialized roles [105].…”

Section: Plant Detection Accuracymentioning

confidence: 99%

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

Volpato

Wright

Gomez

2023

Preprint

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Background Significant effort has been made in manually tracking plant maturity and to measure early-stage plant density, and crop height in experimental breeding plots. Agronomic traits such as relative maturity (RM), stand count (SC) and plant height (PH) are essential to cultivar development, production recommendations and management practices. The use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost. Recent advances in deep learning (DL) approaches have enabled the development of automated high-throughput phenotyping (HTP) systems that can quickly and accurately measure target traits using low-cost RGB drones. In this study, a time series of drone images was employed to estimate dry bean relative maturity (RM) using a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for features extraction and capturing the sequential behavior of time series data. The performance of the Faster-RCNN object detection algorithm was also examined for stand count (SC) assessment during the early growth stages of dry beans. Various factors, such as flight frequencies, image resolution, and data augmentation, along with pseudo-labeling techniques, were investigated to enhance the performance and accuracy of DL models. Traditional methods involving pre-processing of images were also compared to the DL models employed in this study. Moreover, plant architecture was analyzed to extract plant height (PH) using digital surface model (DSM) and point cloud (PC) data sources. Results The CNN-LSTM model demonstrated high performance in predicting the RM of plots across diverse environments and flight datasets, regardless of image size or flight frequency. The DL model consistently outperformed the pre-processing images approach using traditional analysis (LOESS and SEG models), particularly when comparing errors using mean absolute error (MAE), providing less than two days of error in prediction across all environments. When growing degree days (GDD) data was incorporated into the CNN-LSTM model, the performance improved in certain environments, especially under unfavorable environmental conditions or weather stress. However, in other environments, the CNN-LSTM model performed similarly to or slightly better than the CNN-LSTM + GDD model. Consequently, incorporating GDD may not be necessary unless weather conditions are extreme. The Faster R-CNN model employed in this study was successful in accurately identifying bean plants at early growth stages, with correlations between the predicted SC and ground truth (GT) measurements of 0.8. The model performed consistently across various flight altitudes, and its accuracy was better compared to traditional segmentation methods using pre-processing images in OpenCV and the watershed algorithm. An appropriate growth stage should be carefully targeted for optimal results, as well as precise boundary box annotations. On average, the PC data source marginally outperformed the CSM/DSM data to estimating PH, with average correlation results of 0.55 for PC and 0.52 for CSM/DSM. The choice between them may depend on the specific environment and flight conditions, as the PH performance estimation is similar in the analyzed scenarios. However, the ground and vegetation elevation estimates can be optimized by deploying different thresholds and metrics to classify the data and perform the height extraction, respectively. Conclusions The results demonstrate that the CNN-LSTM and Faster R-CNN deep learning models outperforms other state-of-the-art techniques to quantify, respectively, RM and SC. The subtraction method proposed for estimating PH in the absence of accurate ground elevation data yielded results comparable to the difference-based method. In addition, open-source software developed to conduct the PH and RM analyses can contribute greatly to the phenotyping community.

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Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings

Cited by 20 publications

References 37 publications

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

Deep learningversusgeometric morphometrics for archaeobotanical domestication study and subspecific identification

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

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