Organizations: Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA · Panhandle Research and Extension Center, University of Nebraska–Lincoln, Scottsbluff, NE, USA · Department of Entomology, University of Nebraska–Lincoln, Lincoln, NE, USA · Department of Biological Systems Engineering, Florida A&M University, Tallahassee, FL, USA · USDA-ARS, National Soil Erosion Research laboratory, West Lafayette, IN, USA
Insect-induced defoliation threatens grapevine health and yield, yet severity assessment relies on subjective manual scouting. We developed DefoliationCAM (D-CAM), a solar-powered edge AI system that estimates canopy defoliation and transmits results via LoRa to a web dashboard. The pipeline uses YOLOX-Nano to localize grapevine cordons in 4K images and a MobileNet-based classifier with lightweight channel attention to classify 320 x 320 pixel patches. The 29,674 field-collected patches were labeled as background or one of four damage levels: no to negligible, mild, moderate, and severe. Merging moderate and severe classes reduced the class imbalance ratio from approximately 40:1 to 8.8:1. Synthetic augmentation increased macro-F1 from 0.768 to 0.838. The classifier outperformed ResNet-50 in macro-F1 with fewer parameters (1.08 M versus 23.52 M). Patch predictions were aggregated into a canopy-level Non-linear Global Defoliation Severity (NGDS) score and validated against an entomologist's ratings of 86 held-out canopy images. Quadratic discriminant analysis identified boundaries of 9% and 29% separating negligible damage, mild damage requiring monitoring, and action-level damage warranting on-site scouting and insecticide treatment consideration. The 29% boundary aligned with the 25%-30% action threshold range for Japanese beetle management in grapevine. Using TensorRT FP16 on an NVIDIA Jetson Orin Nano, the pipeline processed each 4K image in approximately 1.5 s, with a complete operating cycle of 57-60 s. A five-week deployment of two field units demonstrated the feasibility of autonomous solar-powered operation, LoRa communication, and remote defoliation monitoring. D-CAM supports frequent, objective canopy damage assessment for targeted scouting and timely pest-management decisions.
Figures & tables
Figure 1: Three-phase framework for the development and deployment of D-CAM. (1) scheduled acquisition of 4K grape-canopy images using a solar-powered field unit; (2) dataset preparation, class consolidation, minority-class augmentation, model training, and TensorRT FP16 conversion; and (3) on-device canopy localization, patch-level severity assessment, and LoRa transmission of results to a web dashboard.
Figure 2: System architecture and data flow of D-CAM.
Figure 4: Custom analog battery voltage-sensing circuit. A DC Y-splitter taps the TalentCell 12.8 V battery: one branch powers the Jetson Orin Nano, while the other feeds a resistive divider ( R1=10 k Ω , R2=2.2 k Ω ) whose midpoint ( Vout ) is read on the Maduino (ATmega328) analog pin A1. The divider keeps the source impedance ( ∼ 1.8 k Ω ) below the 10 k Ω ATmega328 ADC limit, and the raw reading is converted to battery voltage via Vbat=ADCraw×0.01461 .
Figure 5: Battery voltage-to-state-of-charge (SOC) calibration curve for the 12.8 V, 6000 mAh LiFePO 4 battery. SOC was estimated by linear interpolation between the calibration points, with voltage readings of 12 V or greater assigned 100% SOC.
Code
Meaning
00
All stages completed successfully
01
Image capture failed (camera/GStreamer error)
02
Model inference failed (TensorRT engine error)
03
Battery voltage read failed (ADC/serial error)
04
LoRa data transmission failed
05
Low-battery power-save
Table 1: Error code system embedded in each 30-byte LoRa payload.
Figure 7: Row (A): real no to negligible damage canopy patches. Row (B): AI-generated damaged images with realistic insect-chewing damage (irregular holes, skeletonized patches, organic edges) via prompt-driven synthesis with randomized hole count (5–9). The synthetic images preserve natural leaf textures and lighting.
Class ID
Label
Count
Proportion (%)
0
Background
10,750
36.2
1
No to negligible
4,736
16.0
2
Mild
12,734
42.9
3
Moderate
1,134
3.9
4
Severe
320
1.1
Total
29,674
100.0
Table 2: Distribution of the 29,674 labeled patches among the five original classes. The moderate damage and severe damage classes were subsequently combined into a single moderate-to-severe damage class, reducing the largest-to-smallest class ratio from approximately ∼ 40:1 to 8.8:1.
Table 3: Test-set performance of the Stage 2 patch-classifier configurations. H-Acc denotes binary healthy-versus-unhealthy accuracy, and D-Acc denotes moderate-to-severe damage-class accuracy.
Backbone
Params (M)
Acc (%)
F1 (macro)
F1 (weighted)
Reference
ResNet-50
23.52
79.15
0.7462
0.7992
[ 45 ]
ResNet-18
11.18
80.24
0.7469
0.8095
[ 45 ]
MobileNetV2
2.23
82.46
0.7745
0.8291
[ 46 ]
MobileNetV3-Small
1.52
82.96
0.7705
0.8337
[ 30 ]
ShuffleNetV2
1.26
81.78
0.7673
0.8231
[ 47 ]
LCA-enhanced (ours)
1.08
83.60
0.8383
0.8341
-
Table 4: Stage 2 patch-classification performance of six backbones trained under the same four-class protocol. The training set was resampled to a target of 2,000 samples per class, with AI-generated patches included in the moderate-to-severe damage class. All models were evaluated on the same held-out, field-collected test set.
Cat 1: No to negligible damage
Cat 2: Mild
Cat 3: Needs action
Avg GT = 7.4%
Avg GT = 17.0%
Avg GT = 47.0%
Panel
Configuration
Pred
∣Δ∣
RMSE
Pred
∣Δ∣
RMSE
Pred
∣Δ∣
RMSE
A
ground truth
7.4
—
—
17.0
—
—
47.0
—
—
B
4-cls, 0% overlap (our deployed model)
6.6
0.8
2.4
16.9
0.1
3.5
46.5
0.5
4.4
C
5-cls, 0% overlap
12.0
4.6
8.0
30.0
13.0
14.0
58.0
11.0
15.0
D
4-cls, 15% overlap
5.9
1.5
2.8
14.0
3.0
4.3
43.0
4.0
8.1
Table 5: Per-category agreement between the predicted NGDS and the expert-derived reference NGDS (GT). Configuration C is the five-class model; the raw input and the four-class configurations (A, B, D–F) are also shown as panels A–E in Fig. 8 . Category definitions: Cat 1 = No to negligible damage ( GT<9% , N=23 ); Cat 2 = Mild ( 9%≤GT≤29% , N=51 ); Cat 3 = Needs action ( GT>29% , N=12 ). Pred = mean predicted NGDS (%); ∣Δ∣ = ∣Pred−GT∣ in percentage points; RMSE per Eq. ( 5 ), in percentage points. Best per category in bold.
Figure 8: Effect of tiling overlap on patch-level severity predictions for a representative canopy image with an expert-derived ground-truth NGDS of 23%. (A) Raw image. Raw input. (B)–(E) Severity maps generated by the LCA-enhanced four-class model using overlap ratios of 0%, 15%, 25%, and 50%, respectively. The corresponding model-derived NGDS values were 25%, 28%, 29%, and 29%.
Figure 9: Distribution of model-derived NGDS values for 86 field images grouped by the entomologist-assigned management category. The QDA-derived decision boundaries define (1) No to negligible damage (NGDS < 9%), (2) Mild damage (9–29%), and (3) Action-level damage ( > 29%).
Figure 10: Visualization across three NGDS categories: no to negligible damage (A), mild (B), and action level damage (C).
Figure 11: Time series of model-derived NGDS values recorded by Unit B in the mature grapevine block during the 2026 field evaluation. Each point represents the NGDS generated during one scheduled operating cycle and transmitted to the web dashboard. The trace shows short-term variability and an increasing frequency of values near 50% later in the evaluation period. Because NGDS is capped at 50% by its formulation, readings at this value represent the upper limit of the metric rather than estimates exceeding 50%.
Figure 12: Battery state-of-charge record for Unit B during the 2026 field evaluation. The orange line shows measurements recorded at 30-min intervals during the daily operating window from 07:00 to 19:00. The blue line represents the daily mean, and the shaded band spans the daily minimum and maximum. Gold dashed lines mark the beginning of each daily operating window at 07:00.
Figure 13: Representative Unit B inference outputs during the 2026 field deployment: (A) August 17, NGDS = 16.9%; (B) September 1, NGDS = 29.5%; and (C) September 11, NGDS = 34.4%. Each example shows the original image (left) and patch predictions (right), with green, yellow, and red representing no to negligible damage, mild, and moderate-to-severe damage, respectively. The increasing NGDS values were consistent with the dashboard trend and observed Japanese beetle feeding damage.
Study
Model
Dataset
Assessment Task
Platform
Camera Vision
Performance
DefoliationCAM
YOLOX-Nano+LCA-enhanced MobileNet classifier (1.08 M)
Field-collected dataset of 29,674 field-collected grape canopy patches with AI-generated damaged-class augmentation
4-class defoliation severity (background, no to negligible damage, mild, moderate-to-severe) aggregated into NGDS score
NVIDIA Jetson Orin Nano (TensorRT FP16)
Field-scale (4K canopy)
83.6% Acc, 0.838 macro-F1; NGDS mapped to three QDA-derived management categories (boundaries at 9% and 29%); ∼ 1.5 s per 4K image
Table 6: Comparison of DefoliationCAM with existing deep-learning approaches for plant defoliation, disease, and pest severity assessment in terms of model, dataset, task, platform, camera vision, and performance.
School of Electrical and Computer Engineering, Cornell University, NY, USA. · Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK. · Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA. +1