2.1. Data acquisition and plant feature extraction

Data acquisition and preprocessing are vital for ML in image processing, with high-resolution imaging, unmanned aerial vehicle (UAV) photography, and 3D scanning providing detailed morphological data for DL model foundation (Shahi et al., Reference Shahi, Xu, Neupane and Guo2022) (Table 1). While high-resolution devices offer superior quality, regular cameras and smartphones provide greater accessibility and scalability, enabling large-scale data collection and enhancing dataset diversity and model robustness.

Table 1

Data acquisition techniques and their applications in plant sciences

Feature extraction in plant image analysis integrates morphology, physiology, genetics, and ecology, starting with colour features (e.g., histograms, coherence vectors) and followed by morphological features (e.g., area, perimeter, shape descriptors) to identify plant traits (Mahajan et al., Reference Mahajan, Raina, Gao and Kant Pandit2021). Texture features, capturing local variations in images, are crucial for species differentiation and disease detection, revealing surface structures like roughness and contrast (Mohan & Peeples, Reference Mohan and Peeples2024). CNNs have proven effective in managing complex plant images, enhancing the classification and detection of diseases through robust feature extraction methods (Ahmad et al., Reference Ahmad, Ashiq, Badshah, Khan and Hussain2022). Additionally, structural features, such as leaf morphology and spatial arrangements, are extracted using techniques like edge detection and shape description (Shoaib et al., Reference Shoaib, Shah, Ei-Sappagh, Ali, Alenezi, Hussain and Ali2023). Lastly, physiological features, including leaf count, size, and vein structure, provide valuable data on plant health and growth dynamics (Bühler et al., Reference Bühler, Rishmawi, Pflugfelder, Huber, Scharr, Hülskamp and Jahnke2015). These features can be obtained manually or automatically through image processing, with recent studies favoring automated methods like segmentation and morphological analysis for high-throughput, objective phenotyping. A key advantage of CNNs is their ability to learn hierarchical features from raw images, eliminating manual feature engineering and enhancing model adaptability and performance in diverse plant phenotyping tasks.

2.2. Preprocessing techniques

Data preprocessing in plant image analysis includes key steps like cropping, resizing, enhancing, augmenting, and annotating to optimize images for ML models. Cropping and resizing standardize dimensions, enhancing computational efficiency and reducing model complexity (Maraveas, Reference Maraveas2024). Data augmentation modifies original images to generate new datasets, with techniques like contrast adjustment, denoising, and sharpening enhancing detail visibility and accuracy (Abebe et al., Reference Abebe, Kim, Kim, Kim and Baek2023), while augmentation strategies like rotation and flipping diversify the dataset to prevent overfitting and improve model generalization (Syarovy et al., Reference Syarovy, Pradiko, Farrasati, Rasyid, Mardiana and Pane2024). Despite their benefits, preprocessing steps can cause information loss, requiring a balance between simplifying the model and preserving critical information. While most preprocessing is not labor-intensive, annotating and labeling training data remains highly labor-intensive, often becoming bottlenecks that hinder project progress. Accurate annotation, often referred to as ‘ground truth’, is essential for supervised learning, as it provides a reliable benchmark for model training and evaluation. In both research and practical applications – such as image recognition, natural language processing, and predictive analytics – the quality of labelled data directly influences model accuracy and reliability (Zhou et al., Reference Zhou, Siegel, Zarecor, Lee, Campbell and Andorf2018a, Reference Zhou, Yan, Han, Li, Geng, Liu and Meyerowitz2018b).

To achieve optimal results, recommended sizes of datasets vary by task complexity. For binary classification, 1,000 to 2,000 images per class are typically sufficient (Singh et al., Reference Singh, Jain, Jain, Kayal, Kumawat and Batra2020). Multi-class classification requires 500 to 1,000 images per class, with higher requirements as the number of classes increases (Mühlenstädt & Frtunikj, Reference Mühlenstädt and Frtunikj2024). More complex tasks, such as object detection, demand larger datasets, often up to 5,000 images per object (Cai et al., Reference Cai, Zhang, Zhu, Zhang, Li and Xue2022). DL models like CNNs generally need 10,000 to 50,000 images, with larger models requiring 100,000+ images (Greydanus & Kobak, Reference Greydanus and Kobak2020). Data augmentation can multiply dataset size by 2–5 times (Shorten & Khoshgoftaar, Reference Shorten and Khoshgoftaar2019). Additionally, Transfer Learning, a machine-learning model, is effective for smaller datasets, requiring as few as 100 to 200 images per class for successful training (Zhu et al., Reference Zhu, Braun, Chiang and Romagnoli2021).

2.3. Commonly used public dataset

The Plant Village dataset is a widely used public resource for DL-based plant disease diagnosis research (Mohameth et al., Reference Mohameth, Bingcai and Sada2020). It serves as a valuable tool in agricultural and plant disease research, offering a comprehensive collection of labelled images essential for developing and testing ML models for plant health monitoring (Pandey et al., Reference Pandey, Tripathi, Singh, Gaur and Gupta2024). Its accessibility, diversity, and standardized format make it a benchmark for algorithm development in precision agriculture, contributing to early disease detection and yield management, and addressing global challenges like food security and sustainable farming (Majji & Kumaravelan, Reference Majji and Kumaravelan2021). Promoting the use of such datasets can enhance collaboration among researchers, standardize methodologies, and support scalable solutions across various agricultural environments (Ahmad et al., Reference Ahmad, Abdullah, Moon and Han2021).

Similar to the plant village dataset, other plant image datasets include the plant doc dataset, which contains images from various plant species for plant disease diagnosis (Singh et al., Reference Singh, Jain, Jain, Kayal, Kumawat and Batra2020). The crop disease dataset features images of diseases in multiple crops, making it suitable for training DL models, especially for crop disease classification (Yuan et al., Reference Yuan, Chen, Ren, Wang and Li2022). The tomato leaf disease dataset focuses on disease images specific to tomato leaves, supporting research in tomato disease recognition and detection (Ahmad et al., Reference Ahmad, Hamid, Yousaf, Shah and Ahmad2020). These datasets are widely used in agriculture, particularly for plant disease detection, crop growth studies, and plant health management, driving the ongoing development of intelligent agricultural technologies.

3.1. The selection of ML model

Image classification, used to categorize input images into predefined groups, is commonly applied in plant identification and disease diagnosis. CNNs, with their strong hierarchical feature extraction abilities, excel in these tasks. Models like AlexNet and ResNet are frequently used to classify plant species and developmental stages (Zhu et al., Reference Zhu, Li, Li, Wu and Yue2018; Malagol et al., Reference Malagol, Rao, Werner, Töpfer and Hausmann2025). ResNet, by incorporating residual learning and skip connections, addresses gradient vanishing and degradation in deep networks. Its enhanced model has been applied in high-throughput quantification of grape leaf trichomes, supporting phenotypic analysis and disease resistance studies. CNN-based models generally achieve over 90% accuracy on public datasets, validating their ‘High’ performance in comparative evaluations (Yu et al., Reference Yu, Luo, Zhou, Zhang, Wu and Ren2021; Yao et al., Reference Yao, Tran, Garg and Sawyer2024).

Simpler models like K-nearest neighbors (K-NN) and support vector machines (SVMs) are ideal for smaller datasets with less complex features. Though computationally efficient and easy to implement, they are more sensitive to noise and tend to perform less effectively on complex image data (Ghosh et al., Reference Ghosh, Singh, Jhanjhi, Masud and Aljahdali2022). K-NN, for instance, classifies samples based on proximity in feature space and can be enhanced using surrogate loss training (Picek et al., Reference Picek, Šulc, Patel and Matas2022). SVMs utilize kernel functions like the Radial Basis Function (RBF) to handle non-linear data and prevent overfitting (Sharma et al., Reference Sharma, Kumar, Gour, Saini, Upadhyay and Kumar2024). Both are typically rated as ‘Medium’ in performance due to their limitations in handling large-scale, high-dimensional data (Azlah et al., Reference Azlah, Chua, Rahmad, Abdullah and Wan Alwi2019).

For object detection tasks, which require both classification and localization, models like Faster R-CNN (FRCNN) and You Only Look Once version 5 (YOLOv5) offer high spatial accuracy. FRCNN uses a region proposal network (RPN) and shared convolutional layers to efficiently predict object categories and bounding boxes (Deepika & Arthi, Reference Deepika and Arthi2022). YOLOv5 enables real-time detection and has been applied to UAV-based monitoring for early detection of pine wilt disease (Yu et al., Reference Yu, Luo, Zhou, Zhang, Wu and Ren2021). In dense slash pine forests, improved FRCNN achieved 95.26% accuracy and an R² of 0.95 in crown detection, showcasing the utility of deep learning for woody plant monitoring (Cai et al., Reference Cai, Xu, Chen, Yang, Weng, Huang and Lou2023).

Other advanced models include deep belief networks (DBNs), which use stacked restricted Boltzmann machines (RBMs) for unsupervised hierarchical learning and are fine-tuned via backpropagation (Lu et al., Reference Lu, Du, Liu, Zhang and Hao2022). Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, are effective for modeling temporal dependencies, such as plant growth simulation using time-lapse imagery (Xing et al., Reference Xing, Wang, Sun, Huang and Lin2023; Liu et al., Reference Liu, Chen, Wu, Han, Bao and Zhang2024a, Reference Liu, Guo, Zheng, Yao, Li and Li2024b). Graph neural networks (GNNs) are increasingly used for modeling complex relationships in plant stress response and gene regulation, although they require significant training effort and parameter tuning (Chang et al., Reference Chang, Jin, Rao and Zhang2024).

In scenarios with limited labelled data or domain shifts, transfer learning is particularly valuable. By leveraging pre-trained models, it enables medium to high performance in plant classification and disease recognition tasks (Wu et al., Reference Wu, Jiang and Cao2022). Advanced architectures like GoogLeNet, with its multi-scale inception module, further enhance classification accuracy. For instance, a GoogLeNet model achieved F-scores of 0.9988 and 0.9982 in classifying broadleaf and coniferous tree species, respectively, after 100 training epochs (Minowa et al., Reference Minowa, Kubota and Nakatsukasa2022).

In summary, each model class exhibits distinct strengths. CNNs specialize in image classification; SVMs and K-NN are optimal for simpler datasets; FRCNN and YOLOv5 excel in object detection; DBNs and RNNs support hierarchical and temporal modeling; GNNs tackle high-dimensional interactions; and transfer learning enhances adaptability across domains. The qualitative performance ratings (High/Medium) presented in Table 2 synthesize evaluation metrics like accuracy, precision, recall, and F1-score across representative studies, enabling researchers to select appropriate models based on task complexity and dataset characteristics.

Table 2

A comparison of common ML models in plant recognition and classification

3.2. The integration of language models

In recent years, with the widespread application of LLMs such as BERT and the GPT series in natural language processing and cross-modal learning, plant science research has also begun exploring the integration of LLMs into areas such as gene function prediction, literature-based knowledge mining, and bioinformatic inference. For instance, LLMs can automatically extract potential functional annotation information from a large body of plant gene literature, aiding in the construction of plant gene regulatory networks. Moreover, due to their powerful contextual understanding capabilities, LLMs demonstrate enhanced accuracy and generalizability in predicting gene expression patterns across species (Zhang et al., Reference Zhang, Ibrahim, Khaskheli, Raza, Zhou and Shamsi2024). The application of LLMs in plant biology is beginning to transform the field by driving advancements in chemical mapping, genetic research, and disease diagnostics (Eftekhari et al., Reference Eftekhari, Serra, Schoeffmann and Portelli2024). For instance, by analyzing data from over 2,500 publications, researchers have revealed the phylogenetic distribution of plant compounds and enabled the creation of systematic chemical maps with improved automation and accuracy (Busta et al., Reference Busta, Hall, Johnson, Schaut, Hanson, Gupta and Maeda2024). LLMs and protein language models (PLMs) also enhance the analysis of nucleic acid and protein sequences, advancing genetic improvements and supporting sustainable agricultural systems (Liu et al., Reference Liu, Chen, Wu, Han, Bao and Zhang2024a, Reference Liu, Guo, Zheng, Yao, Li and Li2024b). In disease diagnostics, models like contrastive language image pre-training (CLIP) utilize high-quality images and textual annotations to improve classification accuracy for plant diseases, achieving significant precision gains on datasets such as plant village and field plant (Eftekhari et al., Reference Eftekhari, Serra, Schoeffmann and Portelli2024).

Similarly, a CNN-based system combining InceptionV3 with GPT-3.5 Turbo achieved 99.85% training accuracy and 88.75% validation accuracy in detecting tomato diseases, providing practical treatment recommendations (Madaki et al., Reference Madaki, Muhammad-Bello and Kusharki2024). The feature fusion contrastive language-image pre-training (FF-CLIP) model further enhances this approach by integrating visual and textual data to identify complex disease textures, achieving a 33.38% improvement in Top-1 accuracy for unseen data in zero-shot plant disease identification (Liaw et al., Reference Liaw, Chai, Lee, Bonnet and Joly2025). These advancements highlight the transformative potential of language models in advancing plant biology research and driving sustainable agricultural innovation.

In summary, two notable new insights brought by LLMs in plant science include: (1) the ability to identify and summarize ‘potential regulatory information within non-coding sequences’, which is often overlooked by traditional models; and (2) the promotion of holistic modeling of plant trait complexity through multimodal integration – such as combining sequence, image, and textual data – offering new avenues for complex trait prediction and breeding design.

3.3. Model training and evaluation methods

Effective ML model training relies on three key components: data partitioning, loss functions, and optimization strategies. Properly splitting the data (commonly 70:15:15 for training, validation, and testing) ensures good generalization (Figure 1). The training set fits model parameters, the validation set guides hyperparameter tuning, and the test set evaluates final performance, enhancing model robustness(Ghazi et al., Reference Ghazi, Yanikoglu and Aptoula2017). For instance, Mohanty et al. used the PlantVillage dataset (54,306 images), applied an 80:10:10 split and cross-entropy loss with SGD optimization, achieving over 99% accuracy and demonstrating the efficiency of deep learning in plant disease identification (Mohanty et al., Reference Mohanty, Hughes and Salathé2016). Similarly, Ferentinos used a publicly available plant image dataset with 87,848 leaf images, splitting it into 80% for training and 20% for testing, achieving 99.53% accuracy on the test set. This study highlights the effectiveness of data partitioning and CNNs in plant species classification and disease detection (Ferentinos, Reference Ferentinos2018).

Figure 1.

CNN and data training process flowchart. A. DL-based image processing flowchart; B. Data training process elowchart.

The loss function quantifies the difference between predicted and true values, minimized during training. For regression tasks, common loss functions include mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), with MSE penalizing larger errors, RMSE providing interpretable results, and MAE being more robust to outliers (Picek et al., Reference Picek, Šulc, Patel and Matas2022). For classification tasks, binary classification problems often use binary cross-entropy to measure the accuracy of predictions involving ‘yes/no’ or ‘true/false’ decisions (Bai et al., Reference Bai, Gu, Liu, Yang, Cai, Wang and Yao2023). Custom loss functions can also be defined to suit specific project needs. For instance, Gillespie et al. developed a deep learning model called ‘Deepbiosphere’ and designed a sampling bias-aware binary cross-entropy loss function, which significantly improved the model’s performance in monitoring changes in rare plant species (Gillespie et al., Reference Gillespie, Ruffley and Exposito-Alonso2024).

Optimization algorithms are equally critical in DL. Common optimizers include stochastic gradient descent (SGD), adaptive moment estimation (ADAM) (Saleem et al., Reference Saleem, Potgieter and Arif2020), and RMSprop (Kanna et al., Reference Kanna, Kumar, Kumar, Changela, Woźniak, Shafi and Ijaz2023). SGD updates parameters using randomly selected samples per iteration, ADAM combines momentum with adaptive learning rates, and RMSprop adjusts the learning rate of each parameter using moving averages, effectively reducing gradient oscillation (Mokhtari et al., Reference Mokhtari, Ahmadnia, Nahavandi and Rasoulzadeh2023). For example, Sun et al. employed the SGD optimizer with momentum techniques in plant disease recognition tasks, which improved the convergence speed and stability of the model, thereby enhancing its accuracy (Sun et al., Reference Sun, Liu, Zhou and Hu2021). In a similar vein, Kavitha et al. trained six ImageNet-pretrained CNN models on an RMP dataset for rural medicinal plant classification and reported that MobileNet, optimized with SGD, achieved the best classification performance, highlighting its effectiveness in medicinal plant recognition (Kavitha et al., Reference Kavitha, Kumar, Naresh, Kalmani, Bamane and Pareek2023). In another study, Labhsetwar et al. compared different optimizers for plant disease classification and found that the Adam optimizer achieved the highest validation accuracy of 98%, demonstrating its strong performance in this context. (Labhsetwar et al., Reference Labhsetwar, Haridas, Panmand, Deshpande, Kolte and Pati2021). Complementing these findings, Praharsha et al. evaluated multiple optimizers in CNNs and found that RMSprop, with a learning rate of 0.001 and L2 regularization of 0.0001, achieved the highest validation accuracy of 89.09%, outperforming Adam and SGD, and proving especially effective for plant pest classification tasks (Praharsha et al., Reference Praharsha, Poulose and Badgujar2024).

Evaluation metrics like accuracy, recall, F1 score, and area under the curve (AUC) are essential for assessing model performance, with accuracy being effective for balanced datasets but potentially misleading for imbalanced data.(Naidu et al., Reference Naidu, Zuva and Sibanda2023). Recall emphasizes the model’s ability to identify all relevant positive cases, essential for tasks like plant species identification, while the F1 score, the harmonic mean of precision and recall, offers a balanced evaluation, particularly for imbalanced datasets (Fourure et al., Reference Fourure, Javaid, Posocco and Tihon2021). AUC evaluates model classification ability across different thresholds and is particularly useful in imbalanced classification tasks (Vakili et al., Reference Vakili, Ghamsari and Rezaei2020). For example, Tariku et al. developed an automated plant species classification system using UAV-captured Red, Green, Blue (RGB) images and transfer learning, achieving 0.99 accuracy, precision, recall, and 0.995 F1 score, highlighting the effectiveness of these metrics in real-world tasks and the importance of recall and F1 score for handling diverse and imbalanced datasets (Tariku et al., Reference Tariku, Ghiglieno, Gilioli, Gentilin, Armiraglio and Serina2023). In another study, Sa et al. introduced WeedMap, a large-scale semantic weed mapping framework using UAV-captured multispectral imagery and deep neural networks, achieving AUCs of 0.839, 0.863, and 0.782 for background, crop, and weed classes, respectively, highlighting the role of AUC in evaluating model performance across multiple categories in real-world agricultural applications (Sa et al., Reference Sa, Popović, Khanna, Chen, Lottes, Liebisch and Siegwart2018).

Evaluation should be conducted after training and before deployment. Re-evaluation is also necessary when there are changes in data distribution or environmental conditions (Reich and Barai, Reference Reich and Barai1999). For example, when encountering new plant species or ecological conditions, retraining or fine-tuning may be needed to maintain strong performance on novel inputs (Soares et al., Reference Soares, Ferreira and Lopes2017).

4.1. Biotic and abiotic stress management

DL models are instrumental in analysing plant leaf images to detect diseases and pests, a vital component of plant protection (Shoaib et al., Reference Shoaib, Shah, Ei-Sappagh, Ali, Alenezi, Hussain and Ali2023). For instance, a 2019 study developed a back propagation neural network (BPNN) – a multilayer feedforward model trained via backpropagation and optimized through one-way ANOVA – that effectively identified rice diseases like blast and blight, highlighting its strength in pattern recognition and classification (Chaudhari & Malathi, Reference Chaudhari and Malathi2023). Additionally, hyperspectral remote sensing combined with ML enables rapid detection of plant viruses like Solanum tuberosum virus Y, enhancing early disease identification (Polder et al., Reference Polder, Blok, De Villiers, Van der Wolf and Kamp2019). The YOLOv5s algorithm processes RGBdrone images for real-time pine wilt disease detection, suitable for large-scale monitoring (Du et al., Reference Du, Wu, Wen, Zheng, Lin and Wu2024). Generative adversarial networks (GANs), consisting of a generator and a discriminator that improve through adversarial learning, have been applied in plant science for tasks such as data augmentation, plant disease detection, and growth simulation (Gandhi et al., Reference Gandhi, Nimbalkar, Yelamanchili and Ponkshe2018).

ML and DL advance abiotic stress management through sensors and drones for early detection, precise predictions, and improved plant resilience (Patil et al., Reference Patil, Kolhar and Jagtap2024). These technologies also optimize plant stress responses, with further advancements expected in agricultural applications (Sharma et al., Reference Sharma, Kumar, Gour, Saini, Upadhyay and Kumar2024). Hyperspectral imaging aids early disease detection related to abiotic stresses (Lowe et al., Reference Lowe, Harrison and French2017). The ‘ASmiR’ framework predicts plant miRNAs under abiotic stresses, supporting stress-resistant crop breeding (Pradhan et al., Reference Pradhan, Meher, Naha, Rao, Kumar, Pal and Gupta2023). GNN, a DL model tailored for graph-structured data, learns node representations via relationships and adjacency, and has been effectively used to predict miRNA associations with abiotic stresses by capturing complex structural patterns (Chang et al., Reference Chang, Jin, Rao and Zhang2024). Interested readers are encouraged to refer to the cited review, which highlights the role of bioinformatics and AI in managing abiotic stresses for food security, aiding stress gene analysis, and improving crop resilience to drought and salinity (Chang et al., Reference Chang, Jin, Rao and Zhang2024).

4.2. Plant species identification and classification

DL aids large-scale growth monitoring, identifying plant growth patterns, health, and predicting yields. An intelligent greenhouse management system uses ML and mobile networks for automated phenotypic monitoring (Rahman et al., Reference Rahman, Shah, Riaz, Kifayat, Moqurrab and Yoo2024). Shapley Additive Explanations (SHAP), which quantifies each feature’s contribution to model predictions, is commonly used to interpret and evaluate the performance of yield prediction models (Sun et al., Reference Sun, Di, Sun, Shen and Lai2019). Lightweight SegNet (LW-SegNet) is a CNN architecture tailored for image segmentation tasks, designed to reduce network parameters and computational demands, ensuring efficient and accurate results, particularly in resource-limited environments. Lightweight networks like LW-SegNet and Lightweight U-Net (LW-Unet) enable efficient segmentation of rice varieties in plant research (Zhang et al., Reference Zhang, Liu and Tie2023a, Reference Zhang, Zhou, Kang, Hu, Heuvelink and Marcelis2023b, Reference Zhang, Sun, Zhang, Yang and Wang2023c). A hybrid model combining RF regression and radiative transfer simulation estimates wheat leaf area index (LAI) using UAV multispectral imaging (Sahoo et al., Reference Sahoo, Gakhar, Rejith, Verrelst, Ranjan, Kondraju and Kumar2023). Self-supervised Learning (SSL) trains models using patterns in unlabelled data without manual labeling, accelerating the training process; while it speeds up plant breeding with unlabelled datasets, supervised pre-training still generally outperforms SSL, particularly in tasks like leaf counting (Ogidi et al., Reference Ogidi, Eramian and Stavness2023) .

CNNs enable fast and accurate plant species identification. In sustainable agriculture, a CNN-based DL model was developed to classify weeds, optimizing herbicide use for eco-friendly control (Corceiro et al., Reference Corceiro, Alibabaei, Assunção, Gaspar and Pereira2023). CNNs (VGG-16, GoogleNet, ResNet-50, ResNet-101) were employed to identify 23 wild grape species, showcasing the effectiveness of DL in leaf recognition and crop variety identification (Pan et al., Reference Pan, Liu, Su, Ju, Fan and Zhang2024).

4.3. Plant growth simulation

ML explores complex molecular and cellular mechanisms in plant growth and development, such as stem cell homeostasis in arabidopsis shoot apical meristems (SAMs) (Hohm et al., Reference Hohm, Zitzler and Simon2010), leaf development (Richardson et al., Reference Richardson, Cheng, Johnston, Kennaway, Conlon and Rebocho2021), and sepal giant cell development (Roeder, Reference Roeder2021), and the simulation of weed growth in crop fields over decades (Zhang et al., Reference Zhang, Liu and Tie2023a, Reference Zhang, Zhou, Kang, Hu, Heuvelink and Marcelis2023b, Reference Zhang, Sun, Zhang, Yang and Wang2023c).

Various algorithms have been applied to study plant development. For example, image processing and ML using SVM and RF were used to analyze Cannabis sativa callus morphology, with SVM showing higher accuracy, while genetic algorithms optimized PGR concentrations to validate the model (Hesami and Jones, Reference Hesami and Jones2021). A microfluidic chip was created to simulate pollen tube growth, and the ‘Physical microenvironment Assay (SPA)’ method was established to study mechanical signal transmission during pollen tube penetration of pistil tissues (Zhou et al., Reference Zhou, Han, Dai, Liu, Gao, Guo and Zhu2023).

Many groups are utilizing the latest computational technologies and algorithms to develop tools that enhance the efficiency and precision of plant biology research (Muller & Martre, Reference Muller and Martre2019). The virtual plant tissue (VPTissue) software simulates plant developmental processes, facilitating the integration of functional modules and cross-model coupling to efficiently simulate cellular-level plant growth (De Vos et al., Reference De Vos, Dzhurakhalov, Stijven, Klosiewicz, Beemster and Broeckhove2017). ADAM-Plant software uses stochastic techniques to simulate breeding plans for self- and cross-pollinated crops, tracking genetic changes across scenarios and supporting diverse population structures, genomic models, and selection strategies for optimized breeding design (Liu et al., Reference Liu, Tessema, Jensen, Cericola, Andersen and Sørensen2019). The L-Py framework, a Python-based L-system simulation tool, simplifies plant architecture simulation and analysis, with dynamic features that enhance programming flexibility, making plant growth model development more convenient (Boudon et al., Reference Boudon, Pradal, Cokelaer, Prusinkiewicz and Godin2012). Many groups are also developing advanced computational tools to accurately simulate plant morphological changes at various stages of growth (Boudon et al., Reference Boudon, Chopard, Ali, Gilles, Hamant and Boudaoud2015). A 3D maize canopy model was created using a t-distribution for the initial model, treating the maize whorl – leaves, stem segments, and buds – as an agent to precisely simulate the canopy’s spatial dynamics and structure (Wu et al., Reference Wu, Wen, Gu, Huang, Wang and Lu2024).

Significant advancements from 2012 to 2023 have enhanced our understanding of plant biology. In 2012, researchers used live imaging combined with computational analysis to monitor cellular and tissue dynamics in A. thaliana (Cunha et al., Reference Cunha, Tarr, Roeder, Altinok, Mjolsness and Meyerowitz2012). The introduction of the Cellzilla platform in 2013 enabled simulation of plant tissue growth at the cellular level (Shapiro et al., Reference Shapiro, Meyerowitz and Mjolsness2013). A pivotal study in 2014 focused on the WOX5-IAA17 feedback loop, which is essential for maintaining the auxin gradient in A. thaliana (Tian et al., Reference Tian, Wabnik, Niu, Li, Yu and Pollmann2014). By 2016, research explored plant signaling pathways and mechanical models to analyze sepal growth and morphology (Hervieux et al., Reference Hervieux, Dumond, Sapala, Routier-Kierzkowska, Kierzkowski and Roeder2016). In 2018, studies delineated the expression pattern of the CLV3 gene in SAMs (Zhou et al., Reference Zhou, Siegel, Zarecor, Lee, Campbell and Andorf2018a, Reference Zhou, Yan, Han, Li, Geng, Liu and Meyerowitz2018b), followed by 2019 research on leaf development and chloroplast ultrastructure (Kierzkowski et al., Reference Kierzkowski, Runions, Vuolo, Strauss, Lymbouridou and Routier-Kierzkowska2019), and the TCX2 gene’s role in maintaining stem cell identity (Clark et al., Reference Clark, Buckner, Fisher, Nelson, Nguyen and Simmons2019). Research in 2020 focused on epidermis-specific transcription factors affecting stem cell niches (Han et al., Reference Han, Yan, Li, Zhu, Feng, Liu and Zhou2020), and 2021 introduced new modeling techniques for root tip growth and stem cell division (Marconi et al., Reference Marconi, Gallemi, Benkova and Wabnik2021). In 2022, 3D bioprinting was used to study cellular dynamics in both A. thaliana and Glycine max (Van den Broeck et al., Reference Van den Broeck, Schwartz, Krishnamoorthy, Tahir, Spurney and Madison2022). The latest studies in 2023 provided new insights into weed evolution and applied advanced DL techniques for plant cell analysis (Feng et al., Reference Feng, Yu, Fang, Jiang, Yang, Chen and Hu2023). These milestones demonstrate the integration of computational tools and empirical datasets in plant science, enabling innovative methods and applications that propel the field forward.

4.4. Plant cell segmentation

Accurate cell segmentation is crucial for understanding plant cell morphology, developmental processes, and tissue organization. Recent advancements in DL and computer vision have led to the development of various specialized tools for segmenting plant cell structures from complex microscopy data. This section provides an overview of key tools, highlighting their core methodologies, applications, and advantages in plant research.

PlantSeg is a neural network-based tool designed for high-resolution plant cell segmentation. It starts with image preprocessing, including scaling and normalization, followed by U-Net-based boundary prediction to identify cell boundaries (Wei et al., Reference Wei, Chen, Yu, Chapman, Melloy and Huang2024). The boundary map is transformed into a region adjacency graph (RAG), where nodes represent image regions and edges represent boundary predictions. Graph segmentation algorithms, such as Multicut or GASP, partition the graph into individual cells, and post-processing ensures the segmentations align with the original resolution and corrects over-segmentation (Wolny et al., Reference Wolny, Cerrone, Vijayan, Tofanelli, Barro and Louveaux2020). By streamlining these processes, PlantSeg supports high-throughput analysis of plant cell dynamics, particularly for confocal and light sheet microscopy data (Vijayan et al., Reference Vijayan, Mody, Yu, Wolny, Cerrone, Strauss and Kreshuk2024). This tool not only improves segmentation efficiency but also handles large-scale datasets, providing robust support for long-term monitoring of plant cell behavior.

Complementing PlantSeg, the Soybean-MVS dataset leverages multi-view stereo (MVS) technology to provide a 3D imaging resource, capturing the full growth cycle of soybeans and enabling precise 3D segmentation of plant organs (Sun et al., Reference Sun, Zhang, Sun, Li, Yu and Miao2023). This dataset plays a significant role in plant growth and developmental research, offering fine-grained data support for dynamic analysis of long-term growth processes.

Other tools focus on generalizability and adaptability across various plant species and imaging modalities. Cellpose utilizes a convolutional neural network (CNN), which performs well in segmenting different cell types and shapes, especially in dynamic plant structures and large-scale image analysis, improving scalability (Stringer et al., Reference Stringer, Wang, Michaelos and Pachitariu2021). This feature enables Cellpose to maintain high accuracy and robustness under diverse experimental conditions when processing plant cells.

DeepCell uses CNNs for plant cell segmentation and supports cell tracking and morphology analysis, offering robust tools for phenotype research. It excels in handling complex cellular dynamics and large-scale datasets, making it ideal for long-term monitoring of plant phenotypes (Greenwald et al., Reference Greenwald, Miller, Moen, Kong, Kagel and Dougherty2022).

Ilastik provides an interactive ML-based segmentation approach, combining flexibility and accuracy. Its user-guided training enables adaptation to diverse plant datasets and experimental conditions, making it valuable for cross-species and multi-modal plant data analysis (Robinson & Vink, Reference Robinson and Vink2024).

Finally, MGX (MorphoGraphX) specializes in 3D morphological analysis of plant tissues by processing 3D microscopy data to visualize and quantify cell shapes, sizes, and spatial patterns. It supports studies on cell interactions and tissue growth, offering precise tools for plant tissue development research (Kerstens et al., Reference Kerstens, Strauss, Smith and Willemsen2020).

In conclusion, despite differences in algorithms and interfaces, these tools collectively advance plant microscopy by minimizing manual segmentation, enhancing reproducibility, and enabling high-throughput and multidimensional analysis. Their complementary strengths offer researchers diverse options, from 2D segmentation to full 3D tissue modeling, tailored to specific experimental needs, and significantly improve efficiency and precision in plant cell and tissue analysis.