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REVIEW 5 major objections 5 minor 110 references

Advancements in Crop Analysis through Deep Learning and Explainable AI

T0 review · 5 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A CNN pipeline is reported to classify five rice grain varieties and four rice leaf diseases with near-perfect accuracy, with LIME and SHAP overlays revealing the image regions behind each prediction.

desk verdict A thesis draft with plausible numbers but untraceable provenance; the crowd-counting leftovers make it impossible to verify what was actually run. read the letter →

arxiv 2508.19307 v1 pith:VZZ4OFCU submitted 2025-08-26 cs.CV cs.AI

Hamza Khan This is my paper · ORCID
classification cs.CVcs.AI
keywords ricegrainclassificationleafdiseasedetectionconvolutionalneuralnetworksexplainableAILIMESHAPResNet-50transferlearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis tries to establish that a deep-learning pipeline can separate five commercially important rice grain varieties (Arborio, Basmati, Ipsala, Jasmine, Karacadag) and four common rice leaf diseases (Bacterial Blight, Blast, Brown Spot, Tungro) with near-perfect accuracy on public image datasets. The reported numbers are per-class F1 scores of 0.986–0.997 for grains and a top disease accuracy of 99.82% from ResNet-50. The authors argue that adding LIME and SHAP explanations makes these automated decisions transparent, showing farmers and quality analysts which visual features drove each classification. If the claim holds, the practical payoff is real: grain grading and early disease diagnosis could move from slow manual inspection to camera-based screening with an audit trail.

What carries the argument

The machinery is a two-stage pipeline. First, a feature extractor: a small custom CNN (two Conv2D blocks, max pooling, a 32-unit dense layer, and a five-unit softmax head) for grains, and pretrained backbones (ResNet-50, VGG16, MobileNet-V2, DenseNet121) for leaf diseases, with Canny edge detection and segmentation applied before training. Second, an explainability stage: LIME constructs local surrogate models to highlight the image regions that pushed a prediction, and SHAP assigns per-pixel additive attributions. The claim is that these two stages together turn a black-box classifier into a transparent crop-analysis tool.

What would settle it

Re-run the described pipeline on the specific public datasets the text names (after locating the correct dataset sources) and check whether per-class F1 scores for the five grains and ResNet-50's 99.82% disease accuracy reproduce; also verify that the cited references actually contain those datasets. Failure to reproduce the confusion matrices or the reported ROC/AUC values would settle the claim.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a single deep-learning workflow—image preprocessing (grayscale conversion, Canny edge detection, segmentation, normalization, rotation and flipping), a custom CNN for grains, pretrained CNNs for diseases, then LIME and SHAP post-hoc explanations—separates the five grain classes with per-class F1 scores between 0.986 and 0.997, and separates the four disease classes with ResNet-50 reaching 99.82% accuracy. The authors take these results to show that standard, non-specialised CNN architectures are sufficient for this level of crop analysis, and that the LIME/SHAP overlays make the basis of each prediction visible to non-experts.

Load-bearing premise

The load-bearing premise is that the pipeline described in the manuscript is the one actually run on the named public datasets; this premise is currently unverifiable because Section 3.3.1 cites the 75K grain dataset as reference [47], which is a MobileNetV3 architecture paper, and Section 3.4.1 cites the 6K disease dataset as reference [77], which is a rice-disease SVM paper, so the provenance of the reported accuracy numbers is not established by the text itself.

Editorial extensions

If this is right

  • If the reported numbers hold, rice grain quality grading could be automated with camera-based CNN inspection, replacing or prioritising manual sorting.
  • Early leaf-disease diagnosis becomes feasible on still images, giving farmers a low-cost second opinion before the disease spreads.
  • The LIME/SHAP overlays give quality analysts a concrete audit trail: each automated decision is accompanied by the image regions that drove it.
  • The model comparison points to ResNet-50 as the strongest of the tested pretrained backbones for this disease dataset, informing future architecture choices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the decisive test for the explainability claim is behavioural—whether LIME/SHAP overlays help a human inspector catch wrong classifications faster or more accurately than a bare confidence score.
  • Beyond the paper: the pipeline should be re-run on field-captured images with varied lighting, angles, and backgrounds; the reported accuracy is on curated public images, and transfer to realistic field conditions is an open question.
  • Beyond the paper: the manuscript's internal inconsistencies in dataset citations leave the provenance of the accuracy numbers unresolved until the exact datasets are located and the experiments are rerun.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The manuscript, submitted as an arXiv preprint in cs.CV, is an unrevised Master's thesis that proposes a deep-learning pipeline for two crop-analysis tasks: classifying five rice grain varieties (Arborio, Basmati, Ipsala, Jasmine, Karacadag) on a 75,000-image dataset, and classifying four rice leaf diseases (Bacterial Blight, Blast, Brown Spot, Tungro) on a 6,000-image dataset. The authors report near-perfect accuracy: F1-scores of 0.986–0.997 for grains (Table 4.4), 99.82% accuracy for ResNet-50 on disease classification (Table 5.4), and AUC = 1.00 for disease classification (Figure 5.3). They augment the classifiers with LIME and SHAP explanations to make predictions transparent. The central load-bearing claims are the accuracy numbers and the interpretability of the decisions.

Significance. If substantiated, the results would be a competent but not methodologically novel application of standard CNN classifiers and post-hoc XAI tools to two public agricultural datasets. The confusion-matrix counts and class-wise scores are internally plausible: the test-set totals are consistent with an 80/10/10 split of 75,000 and 6,000 images, and the reported F1-scores are in line with published results on the same rice-grain dataset. The explicit use of LIME and SHAP side-by-side is a useful practical comparison. However, the manuscript's central empirical claims are not verifiable from the text alone: the datasets are cited to method papers rather than dataset sources, the described preprocessing is not consistently connected to the reported model inputs, and several models discussed in the narrative (InceptionResNetV2, DenseNet201) do not appear in the results tables. No code, data links, or reproducibility artifacts are provided. These issues are load-bearing because the accuracy claims cannot be traced to a concrete, reproducible pipeline.

major comments (5)
  1. [Section 3.3.1 / Reference [47]] The rice grain dataset is claimed to be the 'Rice Image Dataset' on Kaggle, cited as [47], but reference [47] is Howard et al., 'Searching for MobileNetV3' (ICCV 2019), a network-architecture paper, not a dataset. Similarly, Section 3.4.1 cites [77] for the rice leaf disease dataset, but [77] is Sethy et al., a 'Deep feature based rice leaf disease identification using support vector machine' method paper. These are not cosmetic citation errors: the dataset identity (splits, preprocessing, class balance) is the foundation of every accuracy number in Tables 4.4, 5.3, and 5.4. The authors must cite the actual dataset URLs or DOIs and specify the exact versions used; otherwise, the reported numbers cannot be independently checked or reproduced.
  2. [Section 4.1 / Table 4.1 vs. Section 4.2 / Table 4.2] The grain-classification pipeline is internally inconsistent about what the CNN actually saw. Section 3.3.3 describes Canny edge detection, segmentation, normalization, and rotation/flip augmentation, but Table 4.1 infers an input size of '50×50×1' while Table 4.2 states 'Image Size 50x50' with '03 (RGB)' channels. More importantly, the text never states whether the model was trained on raw, edge-detected, segmented, normalized, or augmented images. Figure 4.3 shows the 'Original Image' and LIME overlay, not the edge/segmented version. Without this mapping, the preprocessing descriptions are decorative and the provenance of Table 4.4's numbers is untraceable.
  3. [Section 5.1 / Table 5.1 vs. Section 3.4.3] The disease-classification model description is contradictory across chapters. Section 5.1.2/Table 5.1 specify a 50×50 RGB input for the CNN, while Section 3.4.3 describes the proposed CNN as using '224x224 pixels' RGB input. Table 5.4 reports results for ResNet-50, VGG-16, MobileNet-V2, and DenseNet121, but Chapter 6 states that 'InceptionResNetV2 and DenseNet201 have shown that they are better in reaching high levels of accuracies' without those models appearing in any results table. Figure 5.8 is captioned as the 'Custom CNN' SHAP output, but its architecture and relation to the models in Table 5.4 are unspecified. The reader cannot determine which model produced Table 5.3 or the confusion matrix in Figure 5.2.
  4. [Section 5.3 / Figure 5.3] The abstract and text claim 'outstanding area under the ROC curve in every class' and AUC = 1.00. Figure 5.3 shows a micro-average ROC curve only; no per-class AUC values, confidence intervals, or statistical comparison are provided. On a near-perfect four-class problem, a micro-average AUC of 1.00 is plausible but says nothing about per-class uncertainty, especially with a small test set (≈150 images per class). The claim as stated is not supported by the reported evidence. The authors should report per-class AUC with confidence intervals or bootstrap estimates, or soften the claim.
  5. [Figure 3.5 caption] Figure 3.5, referenced in Section 3.2 as the proposed rice grain classification system, bears the caption 'Proposed architecture for crowd counting using self-supervised training. Stage 1 utilizes the rotation task for self-supervised training, and stage 2 uses distribution matching for density estimation.' This is unrelated to the rice-grain task and appears to be a template artifact. Its presence in the methodology chapter compounds the provenance problem: if the system diagram is not the described system, the reader cannot trust the surrounding experimental description either.
minor comments (5)
  1. [Throughout] The manuscript contains numerous copy-paste artifacts: Chapter 1 refers to 'crowd scene analysis' in the outline (Section 1.8), Section 2.1 says 'A brief review of the current approaches along with their shortcomings for crowd scene analysis,' and Figure 1.6 is captioned 'Significance of Crowd Scene Analysis' while the surrounding text discusses rice grain classification. These should be corrected.
  2. [Table 5.2] The class name 'Burst' appears in Table 5.2, while the text and elsewhere use 'Blast.' This is likely a typo but should be fixed because it is a class label.
  3. [Section 3.3.4 / Table 4.2] The optimizer is described as 'Adamax' in Section 3.3.4 but 'ADAM' in Table 4.2 and Section 4.2. Please state the exact optimizer and learning rate.
  4. [References] References are internally duplicated (e.g., [73] appears twice with different content, [111] duplicates [110]), and several reference numbers do not match the cited content (e.g., [47], [77] as noted above). A systematic reference audit is needed.
  5. [Figures 5.4 and 5.7] Figures 5.4 and 5.7 both have captions saying 'MobileNet-V2' for the LIME visualization, which is certainly a labeling error; one of them presumably corresponds to a different model or should be removed. This makes the XAI comparison confusing.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the accuracy and XAI claims are empirical measurements on external datasets, not results forced by the model definitions or by a self-citation chain.

full rationale

The thesis is an applied deep-learning study: it trains CNN/transfer-learning models on external image datasets and reports measured accuracy, precision, recall, F1, confusion matrices, ROC curves, and LIME/SHAP visualizations. No equation in the paper defines a quantity in terms of the quantity it is supposed to predict, and no parameter fitted to a subset is later relabeled as a prediction. The claims are empirical, not derived. The XAI component is post-hoc interpretation of already-trained models; it does not define the accuracy numbers. The citations to prior work are background/literature and are not invoked as a uniqueness theorem or as the justification for the central empirical claim. The reviewer-identified problems—wrong dataset citations ([47] is a MobileNetV3 paper, [77] is an SVM disease paper), the crowd-counting figure caption (Figure 3.5), contradictory input sizes (50x50x1 vs 50x50x3 vs 224x224), and inconsistent model lists (Tables 5.3/5.4 vs Chapter 6)—are serious provenance, reproducibility, and correctness defects, but they are not circularity: none of them makes an output equal to an input by construction. Accordingly, no circular step is identified and the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest on three classes of unpaid inputs: standard deep-learning machinery (Section 3.1), hand-chosen hyperparameters and split ratios (Tables 4.1 to 4.2, Section 3.3.2), and unverified assumptions about the public datasets' labels and provenance (Sections 3.3.1, 3.4.1). The paper invents no new entities, so the ledger is dominated by modeling choices and data assumptions rather than new physics or new constructs.

free parameters (5)
  • Data split ratio (train/validation/test) = 80/10/10
    Chosen by hand in Section 3.3.2; all reported accuracy and confusion-matrix numbers depend on this split.
  • Best-epoch stopping point (grain model) = epoch 10
    The paper reports validation loss rising after epoch 10 and says training should stop there (Section 4.4, Figure 4.1); reported performance is selected on the validation curve.
  • Grain CNN hyperparameters = 32 and 64 3x3 filters, 2x2 max pooling, dense 32, Adamax/ADAM, L2 regularization
    Hand-chosen architecture (Section 3.3.4, Tables 4.1 to 4.2); the 267,397 parameter count and all accuracy numbers are tied to these choices. The optimizer is named Adamax in Section 3.3.4 but ADAM in Table 4.2.
  • Image input size and channels = 50x50x3 implied, 50x50x1 claimed in Table 4.1
    Table 4.1 infers a grayscale 50x50x1 input, but the parameter count 896 requires 3 input channels and Table 4.2 states RGB; this inconsistency is a hand-specified modeling choice affecting the reported architecture.
  • Data augmentation settings = rotation 90 and 180 degrees, flipping with rate -1 to +1
    Chosen by hand in Section 3.3.3.4; the vague 'rate -1 to +1' flip description is not reproducible as written.
assumptions (4)
  • standard math Standard deep learning machinery (backpropagation, SGD/Adam convergence, softmax classification) works as taught.
    Invoked throughout Section 3.1 with textbook descriptions; the results inherit all standard assumptions of supervised CNN training.
  • domain assumption The publicly available Kaggle images of isolated grains and leaves are correctly labeled and representative of the five varieties and four diseases.
    Introduced in Sections 3.3.1 and 3.4.1; label quality is never audited and the dataset citations are wrong ([47] is a MobileNetV3 paper, [77] is a rice-disease SVM paper), so this premise is unverifiable.
  • domain assumption Morphological image features (size, shape, color, texture) captured in the images are sufficient to separate all five grain varieties and four diseases.
    The classification premise stated in Sections 1.1 and 3.1.3; if varieties are confounded in image space, the reported near-perfect accuracies overstate real-world separability.
  • ad hoc to paper The described experimental pipeline (Canny edge detection, segmentation, normalization, augmentation, then CNN training) is the pipeline actually run.
    Figure 3.5 in the methodology is captioned as a crowd-counting, density-estimation architecture, so the pipeline description cannot be taken at face value; the central results inherit this uncertainty.

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Cite this review

Pith. "Pith review of Advancements in Crop Analysis through Deep Learning and Explainable AI." pith.science (2026). https://pith.science/paper/VZZ4OFCU

@misc{pith2026250819307,
  author       = {Pith},
  title        = {Pith review of: Advancements in Crop Analysis through Deep Learning and Explainable AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VZZ4OFCU}},
  note         = {Machine review of arXiv:2508.19307}
}
read the original abstract

Rice is a staple food of global importance in terms of trade, nutrition, and economic growth. Among Asian nations such as China, India, Pakistan, Thailand, Vietnam and Indonesia are leading producers of both long and short grain varieties, including basmati, jasmine, arborio, ipsala, and kainat saila. To ensure consumer satisfaction and strengthen national reputations, monitoring rice crops and grain quality is essential. Manual inspection, however, is labour intensive, time consuming and error prone, highlighting the need for automated solutions for quality control and yield improvement. This study proposes an automated approach to classify five rice grain varieties using Convolutional Neural Networks (CNN). A publicly available dataset of 75000 images was used for training and testing. Model evaluation employed accuracy, recall, precision, F1-score, ROC curves, and confusion matrices. Results demonstrated high classification accuracy with minimal misclassifications, confirming the model effectiveness in distinguishing rice varieties. In addition, an accurate diagnostic method for rice leaf diseases such as Brown Spot, Blast, Bacterial Blight, and Tungro was developed. The framework combined explainable artificial intelligence (XAI) with deep learning models including CNN, VGG16, ResNet50, and MobileNetV2. Explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) revealed how specific grain and leaf features influenced predictions, enhancing model transparency and reliability. The findings demonstrate the strong potential of deep learning in agricultural applications, paving the way for robust, interpretable systems that can support automated crop quality inspection and disease diagnosis, ultimately benefiting farmers, consumers, and the agricultural economy.

Figures

Figures reproduced from arXiv: 2508.19307 by the authors.

Figure 1.1
Figure 1.1. A general picture depicting different types of products generated from crops .................... 2 [PITH_FULL_IMAGE:figures/full_fig_p015_1_1.png] view at source ↗
Figure 1.1
Figure 1.1. A general picture depicting different types of products generated from crops [PITH_FULL_IMAGE:figures/full_fig_p020_1_1.png] view at source ↗
Figure 1.2
Figure 1.2. A general picture depicting crops with different diseases [PITH_FULL_IMAGE:figures/full_fig_p021_1_2.png] view at source ↗
Figures from the paper (35 more)
Figure 1.3
Figure 1.3. Figure 1.3: Different Varieties of Rice grain (a) Arborio Rice Grains (b) Basmati Rice Grains (c) Ipsala [PITH_FULL_IMAGE:figures/full_fig_p025_1_3.png]
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p025_1.png]
Figure 1.4
Figure 1.4. Figure 1.4: Block Diagram of Proposed Rice Grain Classification System [PITH_FULL_IMAGE:figures/full_fig_p026_1_4.png]
Figure 1.5
Figure 1.5. Figure 1.5: Different Diseases of Rice Crop (a) Tungro Disease (b) Bacterial Blight (c) Blast Disease (d) [PITH_FULL_IMAGE:figures/full_fig_p029_1_5.png]
Figure 1.6
Figure 1.6. Figure 1.6: Significance of Crowd Scene Analysis Significance of Rice Grain Classification Quality Assurance Market Differentiation Efficient Supply Chain Research and Development Food Security [PITH_FULL_IMAGE:figures/full_fig_p031_1_6.png]
Figure 1.7
Figure 1.7. Figure 1.7: Significance of Rice Crop Disease Detection [PITH_FULL_IMAGE:figures/full_fig_p032_1_7.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p048_3.png]
Figure 3.2
Figure 3.2. Figure 3.2: General Architecture of Artificial Neural Network [PITH_FULL_IMAGE:figures/full_fig_p049_3_2.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p051_3.png]
Figure 3.3
Figure 3.3. Figure 3.3: Backpropagated Neural Networks – How it works? [PITH_FULL_IMAGE:figures/full_fig_p051_3_3.png]
Figure 3
Figure 3. Figure 3: ) [PITH_FULL_IMAGE:figures/full_fig_p052_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: General Architecture of Convolutional Neural Network [PITH_FULL_IMAGE:figures/full_fig_p052_3_4.png]
Figure 3.5
Figure 3.5. Figure 3.5: Proposed architecture for classification of different varieties of rice grains. [PITH_FULL_IMAGE:figures/full_fig_p056_3_5.png]
Figure 3.6
Figure 3.6. Figure 3.6: Class Distribution of Different Varieties of Rice Grains [PITH_FULL_IMAGE:figures/full_fig_p057_3_6.png]
Figure 3.7
Figure 3.7. Figure 3.7: Dataset Distribution after Splitting 3.3.3 Image Preprocessing: 3.3.3.1 Edge Detection: To efficiently identify grain boundaries in images (as shown in [PITH_FULL_IMAGE:figures/full_fig_p057_3_7.png]
Figure 3.8
Figure 3.8. Figure 3.8: Rice grains with detected boundaries The existence of lines around rice grains after Canny edge detection can be explained by low threshold configurations, noise, and faint gradients that cause extraneous edges to appear. By using Gaussian blurring to reduce noise, n…
Figure 3.9
Figure 3.9. Figure 3.9: Rice grains with detected edges, highlighting the contours and characteristics essential for [PITH_FULL_IMAGE:figures/full_fig_p059_3_9.png]
Figure 3.10
Figure 3.10. Figure 3.10: Segmented images of rice grains for enhanced analysis [PITH_FULL_IMAGE:figures/full_fig_p060_3_10.png]
Figure 3.11
Figure 3.11. Figure 3.11: Architecture of Proposed Model for Rice Grain Classification [PITH_FULL_IMAGE:figures/full_fig_p061_3_11.png]
Figure 3
Figure 3. Figure 3: ) [PITH_FULL_IMAGE:figures/full_fig_p062_3.png]
Figure 3.12
Figure 3.12. Figure 3.12: Class Distribution of Different Varieties of Rice Grains [PITH_FULL_IMAGE:figures/full_fig_p063_3_12.png]
Figure 3.13
Figure 3.13. Figure 3.13: Examples of Rice Crop Diseases: Bacterial Blight, Blast, Tungro, and Brown Spot as [PITH_FULL_IMAGE:figures/full_fig_p063_3_13.png]
Figure 3.14
Figure 3.14. Figure 3.14: Architecture of Proposed Model for Rice Crop Disease Classification [PITH_FULL_IMAGE:figures/full_fig_p064_3_14.png]
Figure 4.1
Figure 4.1. Figure 4.1: Training loss and accuracy curves illustrating model performance over epochs [PITH_FULL_IMAGE:figures/full_fig_p068_4_1.png]
Figure 4.2
Figure 4.2. Figure 4.2: Confusion matrix displaying classification results and model performance [PITH_FULL_IMAGE:figures/full_fig_p069_4_2.png]
Figure 4.3
Figure 4.3. Figure 4.3: Original Image vs. LIME-generated visualization highlighting influential features (a) Basmati [PITH_FULL_IMAGE:figures/full_fig_p071_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: SHAP-generated output highlights the key features; (a) Basmati rice grain, (b) Arborio Rice [PITH_FULL_IMAGE:figures/full_fig_p072_4_4.png]
Figure 5.1
Figure 5.1. Figure 5.1: Training loss and accuracy curves illustrating model performance over epochs [PITH_FULL_IMAGE:figures/full_fig_p075_5_1.png]
Figure 5.2
Figure 5.2. Figure 5.2: Confusion matrix displaying classification results and model performance for Rice Diseases [PITH_FULL_IMAGE:figures/full_fig_p076_5_2.png]
Figure 5.3
Figure 5.3. Figure 5.3: RoC for Crop disease Classification [PITH_FULL_IMAGE:figures/full_fig_p076_5_3.png]
Figure 5.4
Figure 5.4. Figure 5.4: Original Image vs. LIME-generated visualization using MobileNet-V2 highlighting influential [PITH_FULL_IMAGE:figures/full_fig_p078_5_4.png]
Figure 5.5
Figure 5.5. Figure 5.5: Original Image vs. LIME-generated visualization using VGG16 highlighting influential [PITH_FULL_IMAGE:figures/full_fig_p079_5_5.png]
Figure 5.6
Figure 5.6. Figure 5.6: Original Image vs. LIME-generated visualization using RESNET-50 highlighting influential [PITH_FULL_IMAGE:figures/full_fig_p079_5_6.png]
Figure 5.7
Figure 5.7. Figure 5.7: Original Image vs. LIME-generated visualization using MobileNet-V2 highlighting influential [PITH_FULL_IMAGE:figures/full_fig_p080_5_7.png]
Figure 5.8
Figure 5.8. Figure 5.8: SHAP-generated output using Custom CNN highlights the key features; (a) Leaves with [PITH_FULL_IMAGE:figures/full_fig_p081_5_8.png]

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.