VineGuard-AI: An Integrated Deep-Learning Framework for Simultaneous Vineyard Canopy Quantification and Foliar Disease Classification

VineGuard-AI: An Integrated Deep-Learning Framework for Simultaneous Vineyard Canopy Quantification and Foliar Disease Classification

1. Prof. M D Ingle

   Dept. of Computer Engineering JSPM's JSCOE, Pune

2. Jay Prakash Mane

   Dept. of Computer Engineering JSPM's JSCOE, Pune

3. Shailesh S. Patil

   Dept. of Computer Engineering JSPM's JSCOE, Pune

4. Jay Sharad Patil

   Dept of Computer Engineering JSPMs JSCOE , Pune

5. Utkal Santosh Pansare Dept of Computer Engineering JSPMs JSCOE , Pune

   Abstract – Automated plant health monitoring has emerged as a critical enabler of precision viticulture, driven by the inadequacy of manual, expert-dependent inspection methods that fail to scale across large vineyard plantings [1]. This paper presents VineGuard-AI, a two-stage computer- vision pipeline that simultaneously estimates vineyard canopy coverage and diagnoses foliar diseases from a single uploaded photograph. The canopy-estimation stage employs a U-Net encoder-decoder architecture [5] trained from scratch on 587 manually annotated canopy images, yielding a binary vegetation mask from which a precise coverage percentage is computed. The disease-recognition stage fine- tunes an EfficientNet-B4 backbone [7] pretrained on ImageNet [15] to classify four grape-specific conditions: Black Rot, Esca (Black Measles), Leaf Blight, and Healthy. Unlike prior single-task classifiers that degrade significantly under field conditions [1][2], the proposed framework integrates a colour-heuristic blending term to improve robustness against illumination variability. A deterministic severity estimator and a rule-based agronomic advisory module generate actionable treatment and pruning recommendations. On held-out test data, VineGuard-AI achieves segmentation IoU 0.85, Dice 0.92, and weighted F1 0.93results competitive with grape-specific state-of- the-art methods [3][4] while additionally providing canopy quantification absent from most comparable systems [6]. End-to-end CPU inference completes in under 3.2 seconds, confirming suitability for resource-constrained rural deployment. The modular architecture supports future integration with UAV imagery, IoT sensor networks, and mobile platforms.

   Keywordsgrapevine disease detection; semantic segmentation; U-Net; EfficientNet-B4; transfer learning; canopy estimation; precision viticulture; deep learning

   1. INTRODUCTION

      Indian viticulture, heavily concentrated in Maharashtra, contributes substantially to national horticultural output yet remains exposed to fungal foliar infectionsprincipally Black Rot (Guignardia bidwellii), Esca (Phaeomoniella chlamydospora), and Leaf Blight (Pseudocercospora vitis) capable of reducing yields by 2080% when intervention is delayed [3].

      The dominant diagnostic practice, manual field scouting by a trained agronomist, does not scale across large plantings and typically flags disease only after symptoms are visually pronounced. Deep CNNs have demonstrated transformative potential for automated plant-disease identification; however, most deployed systems target a single diagnostic task and omit the canopy density metrics that directly inform pruning and irrigation scheduling.

      This paper proposes VineGuard-AI to address both gaps: a pixel-level canopy segmentation network paired with a grape- specific disease classifier, augmented by severity scores and treatment recommendations. Key contributions include:

      • Custom U-Net trained on 587 annotated images for canopycoverage estimation.

      • Fine-tuned EfficientNet-B4 with colour-heuristic blendingfor four-class disease recognition.

      • Integrated severity estimator and rule-based advisoryengine.

      • CPU inference <3.2 s, enabling resource-constrained ruraldeployment.

   2. RELATED WORK

      Mohanty et al. [1] showed that CNNs trained on the PlantVillage corpus achieved >99% accuracy under laboratory imaging but fell to ~31% on field photographs, motivating the domain-specific preprocessing adopted here. Ferentinos [2] compared AlexNet, GoogLeNet, VGG, and Overfeat, with VGG achieving 99.53%, establishing ImageNet transfer learning as the standard foundation for plant-disease models.

      Among grape-specific studies, Liu et al. [3] reported 94.83% accuracy with a mobile CNN across four disease classes, and Barman et al. [4] obtained 96.4% using Inception-V3, confirming that crop-specific fine-tuned models outperform general multi- crop architectures. On the segmentation side, Ronneberger et al.

      [5] introduced U-Net for biomedical segmentation, later adopted widely for agricultural pixel-level tasks. Sa et al. [6] demonstrated >90% canopy coverage accuracy on vineyard UAV imagery. Tan and Le [7] proposed EfficientNet; Atila et al. [13] confirmed B4 as the best accuracy/inference trade-off for plant- leaf disease classification on CPU-bound systems.

      TABLE I

      VineGuard-AI vs. Representative Prior Work

      Work	Scope	Canopy+Sev?
      Liu et al. [3]	Grape (4 cls.)	No
      Barman et al. [4]	Grape disease	No
      Sa et al. [6]	Canopy only	Partial
      VineGuard-AI	Disease+Canopy+Sev	Yes

   3. PROPOSED METHODOLOGY

      1. System Pipeline

         An uploaded photograph is processed in parallel by two neural networks. GrapeCanopyNet (U-Net branch) produces a binary vegetation mask from which canopy coverage percentage is computed; GrapeLeafCNN (EfficientNet-B4 branch) assigns one of four disease labels with a confidence score. Both outputs feed a severity estimator and a leaf-feature extraction module, whose results drive a rule-based recommendation engine. Preprocessing applies bilinear resizing (572×572 for U-Net,

         380×380 for EfficientNet-B4) and per-channel ImageNet- standard normalisation. Training augmentation includes random flips, ±30° rotations, and brightness jitter.

      2. Canopy Segmentation GrapeCanopyNet

         The segmentation model follows the standard U-Net [5] encoder-decoder design: four downsampling blocks (Conv 3×3

         –+ BN –+ ReLU –+ MaxPool 2×2) expand feature depth from 64 to 1024 channels at the bottleneck, mirrored by four upsampling blocks with transposed-convolution and skip-connection

         concatenation. The network is trained from random initialisation for 30 epochs (batch size 4) on 587 manually annotated images

         -4 -5

         via Adam (lr = 10 , wd = 10 ) under a composite loss:

         L = L + L

         BCE Dice

         where BCE penalises pixel-wise error and Dice Loss addresses class imbalance:

         L = 1 – (2|YnP| + E) / (|Y| + |P| + E)

         Dice

         Segmentation quality is evaluated using IoU = |YnP| / |YuP|.

         The model contains ~31 million parameters.

      3. Disease Classification GrapeLeafCNN

         Disease recognition uses an EfficientNet-B4 backbone [7] pretrained on ImageNet [15], with early blocks (04) frozen during fine-tuning. The 792-dimensional pooled feature vector passes through a custom head: Dropout(0.3) –+ FC(512) –+ BN –+ ReLU –+ Dropout(0.2) –+ FC(128) –+ FC(4) –+ Softmax, yielding:

         P(class |x) = exp(z ) / "i exp(z )

         To counter illumination variability [1], the final prediction blends CNN output p with a colour-heuristic score vector CNN

         derived from disease-specific pixel ratios (brown necrosis, norm

         chlorosis, edge browning, green density):

         p = (0.55·p + 0.45·h ) / · final CNN norm 1

         Blend weights were determined by grid search on a held-out validation split.

      4. Severity Estimation and Recommendation Engine

      A pixel-level colour-analysis routine quantifies the percentage of leaf area

      exhibiting disease-specific signatures and assigns a label: Mild (<20%), Moderate (2040%), Severe (4060%), or

      Critical (>60%). Eight quantitative descriptorsvegetation index (NDVI = (G-R)/(G+R+E)),

      proxy

      chlorophyll content, lesion density, yellowing, necrosis fraction, water-content proxy, texture roughness, and an overall health scoreare computed from raw pixel arrays and used with the predicted class to retrieve structured treatment and pruning advice from a domain knowledge base

   4. RESULTS AND DISCUSSION

      TABLE II

      VineGuard-AI Performance Summary

      Metric	Value
      Segmentation IoU	0.85
      Segmentation Dice	0.92
      Weighted F1	0.93
      F1 Black Rot	0.940
      F1 Esca	0.920
      F1 Leaf Blight	0.915
      F1 Healthy	0.955
      CPU Inference	<3.2 s

      The segmentation network achieved validation IoU = 0.85 and Dice = 0.92 after 30 training epochs (Table II). The higher Dice relative to IoU reflects robust boundary capture despite canopy shape variability and illumination changes. The small training-to- validation loss gap confirms limited overfitting given the 587- image datasetattributed to the combined BCE+Dice objective and the augmentation regime.

      For disease classification, the weighted F1 of 0.93 reflects balanced precision and recall across all classes. The Healthy class achieved the highest per-class score (0.955), indicating reliable discrimination of disease-free leaves. The lowest score (Leaf Blight, 0.915) arises from visual overlap with Esca interveinal chlorosisa direction for future refinement of the heuristic blending weight for this class.

      These results are competitive with single-task grape-disease classifiers [3][4][13] while additionally providing canopy quantification absent from most comparable systems [6]. CPU inference in under 3.2 seconds confirms that GPU acceleration is not required for practical deploymenta critical constraint in rural Indian vineyard contexts where high-end hardware is unavailable.

   5. CONCLUSION

This paper presented VineGuard-AI, a dual-network computer-vision pipeline that jointly estimates vineyard canopy coverage and diagnoses four grape foliar diseases from a single photograph, augmented with severity scoring and agronomic recommendations. Experimental resultsIoU 0.85 for segmentation and weighted F1 0.93 for disease classification demonstrate that a tightly scoped, crop-specific pipeline can

deliver accuracy competitive with broader systems while providing more directly actionable guidance for growers.

Future work will extend disease coverage to Downy Mildew and Powdery Mildew, incorporate field-scale UAV canopy imagery, evaluate lightweight MobileNet variants for on-device mobile inference, and conduct a structured field trial with Maharashtra vineyard cooperatives to validate real-world diagnostic performance against agronomist ground truth.

Acknowledgment

The authors thank the project guide and the Department of Computer Engineering, JSPM's JSCOE, Pune, for their guidance and access to computing resources.

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