Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models

Phongsakon Mark Konrad; Casper Kunstmann-Olsen; Jacek Fiutowski; Serkan Ayvaz

eess.IVApr 5, 2026

Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models

Authors: Phongsakon Mark Konrad, Casper Kunstmann-Olsen, Jacek Fiutowski, Serkan Ayvaz

Organizations: The Maersk Mc-Kinney Moller Institute, SDU Centre for Industrial Software, University of Southern Denmark, Sønderborg, 6400, Denmark · Mads Clausen Institute, SDU NanoSyd, University of Southern Denmark, Sønderborg, 6400, Denmark

Abstract

Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computationally intensive deep learning models. These systems typically require GPU resources, large-scale training data, and domain expertise, limiting their feasibility for many real-world agricultural settings. This study systematically evaluates 20 classical machine learning algorithms on hyperspectral imaging data for simultaneous ripeness classification and firmness prediction across five fruit species, using cross-validated experimental design with Bayesian hyperparameter optimization. Data preprocessing strategy, particularly class balancing and spectral transformations, contributes as much to prediction accuracy as algorithm choice. Our results show that tree-based machine learning models can outperform state-of-the-art deep earning models reported in Fruit-HSNet. Moreover, the findings indicate that only three visible-range wavelengths are needed to recover over 94% of full-spectrum accuracy, demonstrating that low-cost multispectral sensors combined with lightweight machine learning models can serve as practical alternatives to expensive hyperspectral cameras and complex deep learning approaches for practical fruit quality sorting.

Figures & tables

Figure 1: Dataset overview. (a) Sample distribution across five fruit types and three ripeness classes. (b) PCA cumulative variance: 18 components capture 95% of variance in the 1,120-dimensional feature space. (c) PCA projection and (d) t-SNE projection colored by fruit type show species-driven cluster structure rather than quality-state separation.

Figure 2: Five spectral preprocessing types (rows) across the 224 bands (400–1000 nm), grouped by fruit type, ripeness state, and firmness class (columns). Shaded bands: mean

\pm

std. Rows: (a–c) raw, (d–f) 1st derivative, (g–i) continuum removed, (j–l) SNV, (m–o) D1(CR). Inter-species variation dominates; class separation is clearest in derivative features near the red edge (

\sim

700 nm).

Panel of Figure 3 — Figure 3: Six-phase benchmark pipeline. Each phase trains independent ripeness and firmness models, combined via overall accuracy (OA). Dashed arrows indicate configuration propagation between phases.

Rk	Model	Family	P1 $\uparrow$	P2 $\uparrow$	P2 Config	P3 $\uparrow$	P3 CI	P4 CV (%) $\uparrow$	Time $\downarrow$	Size $\downarrow$
			OA	OA		OA	[95%]		(s)	(MB)
1	XGBoost	Boosting	67.4	67.4	SMOTE / No PCA	65.6	[57.0, 72.7]	82.3 $\pm$ 5.4	5.9	0.72
2	HistGradientBoosting	Boosting	65.2	68.1	SMOTE / No PCA	67.4	[59.2, 74.6]	82.2 $\pm$ 5.4	4.6	5.05
3	LGBM	Boosting	68.1	69.2	Strat. Resplit / No PCA	69.2	[61.4, 76.6]	82.2 $\pm$ 4.9	4.7	1.64
4	ExtraTrees	Tree-based	71.7	75.0	Strat. Resplit / No PCA	75.0	[67.6, 81.8]	82.2 $\pm$ 4.7	0.2	2.77
5	SVC_RBF	SVM	56.2	57.6	SMOTE / No PCA	56.9	[48.2, 64.5]	81.1 $\pm$ 4.0	0.6	5.16

Table 1: Model performance and efficiency across pipeline phases (full-spectrum, 1,120 features). Models sorted by Phase 4 cross-validated overall accuracy. 11 11 11 P3 and P4 rankings correlate at Spearman

\rho=0.51

(

p=0.020

); P4 cross-validated rankings are authoritative. P3 CIs are Wilson 95% intervals (

n

=138). Protocol differences with Fruit-HSNet are documented in Section 3 . CO 2 emissions negligible (

<

0.1 g per model); column omitted. The P2 Config column shows the per-model best Phase 2 configuration; Phase 3 optimization uses a single global best configuration (stratified resplit / no PCA) for all models. PLS-DA P4 CV metrics should be interpreted with caution: a pipeline anomaly produced identical per-fold ripeness and firmness accuracies, likely due to incorrect task separation during cross-validation (see Section 5 ). P4 shows 10-fold stratified cross-validation accuracy (mean

\pm

std). P3 Wilson 95% CI (binomial,

n

=138) shown in brackets. Bold = best in column; underline = second-best. Arrows indicate preferred direction.

Rank	Model	Avg. Rank $\downarrow$	Mean F1 $\uparrow$	Family
1	ET	3.7	0.792 $\pm$ 0.077	Tree-based
2	SVM-R	4.7	0.787 $\pm$ 0.077	SVM
3	KNN	5.4	0.771 $\pm$ 0.061	Other
4	GBC	5.8	0.760 $\pm$ 0.064	Boosting
5	RF	6.2	0.758 $\pm$ 0.063	Tree-based
6	LGBM	6.2	0.755 $\pm$ 0.049	Boosting

Table 2: Model rankings from 10-fold cross-validation (Friedman/Nemenyi,

\alpha=0.05

). Rank = mean rank across folds (lower is better). Models connected by a bar in Fig. 1 are not significantly different.

Comparison	Summary	$d$	Effect
Friedman (20 classifiers)	$\chi^{2}(19)=144.8$ , $p<10^{-20}$	—	—
Nemenyi pairs ( $\alpha$ =0.05)	46 of 190 pairs differ	—	—
Firmness $>$ Ripeness	19/20 models; mean gap 25.0 pp	1.84	Large
(IQR: 21.4–32.8 pp; exception: LDA)
HSI vs. RGB	Cohen’s $d$ from Phase 4 fold F1	1.17	Large
HSI vs. VIS-3	Phase 3 OA, 20 models; $p=0.46$	0.32	Small

Table 3: Summary of statistical tests and descriptive comparisons. Effect sizes follow Cohen’s conventions:

|d|<0.2

negligible,

0.2

–

0.5

small,

0.5

–

0.8

medium,

>0.8

large. Friedman and Nemenyi tests are appropriate because models share the same CV folds. Firmness vs. ripeness comparisons involve the same 20 models trained on shared data; formal significance tests are not used because the observations are not independent. Phase 3 modality comparisons (HSI vs. VIS-3, HSI vs. RGB, VIS-3 vs. RGB) use paired Wilcoxon signed-rank tests on per-model overall accuracy.

Figure 4: Pipeline performance. (a) Phase progression for all 20 models: connected dots trace each model’s overall accuracy from baseline (P1) through ablation (P2, best config), optimization (P3), and 10-fold cross-validation (P4). Top-5 models highlighted; remaining in gray. The largest gains occur between P1 and P2, confirming that preprocessing contributes more than hyperparameter tuning for competitive models. (b) Training-time efficiency (log scale) vs overall accuracy (Phase 3), colored by algorithmic family. Marker size reflects model storage size. Star markers denote Pareto-optimal models in model size vs accuracy space (6 models: ExtraTrees, RandomForest, XGBoost, GradientBoosting, AdaBoost, DecisionTree), representing the best size-accuracy tradeoff frontier. Tree-based methods achieve the highest accuracy with the shortest training times.

Balancing strategy	No PCA	PCA	$\Delta$ (pp)
Stratified resplit †	59.08 $\pm$ 9.26	57.41 $\pm$ 4.57	+1.67
SMOTE	57.14 $\pm$ 10.61	53.84 $\pm$ 7.18	+3.30
Original unbalanced	58.32 $\pm$ 9.38	56.29 $\pm$ 6.59	+2.03
Random oversampling	57.01 $\pm$ 9.85	58.12 $\pm$ 4.47	$-$ 1.11
Random undersampling	46.56 $\pm$ 8.93	40.13 $\pm$ 7.47	+6.43
Overall mean	55.62	53.16	+2.46

Table 4: Phase 2 ablation: mean

\pm

SD overall accuracy (%)

\uparrow

across 20 models by balancing strategy and PCA setting.

\Delta

= no-PCA minus PCA difference. Best strategy in bold. Large SDs (4–11 pp) reflect substantial model-to-model variance within each configuration. † Stratified resplit replaces the training set (

n

=414 vs

n

=381); strategies below the rule augment the original training set.

Figure 5: Phase 2 preprocessing ablation across 200 configurations. Overall accuracy (%) for 20 models (rows, sorted by best OA) across 10 preprocessing configurations (5 balancing strategies $\times$ 2 PCA settings). Vertical white lines separate balancing strategies; within each pair, the left column is without PCA and the right is with PCA. The stratified resplit / no PCA column consistently achieves the highest values.

Rk	Model	RA $\uparrow$	FA $\uparrow$	OA $\uparrow$	VIS-3	RGB	$\Delta$ OA
		Full-spectrum			Reduced-band OA $\uparrow$
1	ExtraTrees	63.0	87.0	75.0	71.0	72.1	+2.9
2	RandomForest	60.9	86.2	73.6	67.8	71.4	+2.2
3	LGBM	58.0	80.4	69.2	68.1	68.8	+0.4
4	HistGradBoosting	54.3	80.4	67.4	67.4	69.9	$-$ 2.5
5	XGBoost	52.2	79.0	65.6	68.5	68.5	$-$ 2.9

Table 5: Task asymmetry and modality comparison of Phase 3 models. RA = ripeness accuracy, FA = firmness accuracy, OA = overall accuracy. Full-spectrum has 1,120 features; VIS-3 and RGB each use 15 features.

\Delta

OA = Full OA

-

max(VIS-3, RGB).

Figure 6: Task asymmetry, modality comparison, and spectral importance for Phase 3 optimized models. (a) Task asymmetry: ripeness (pink) vs firmness (green) accuracy per model, revealing a persistent gap across all 20 algorithms. (b) Modality comparison: full-spectrum HSI (blue) vs VIS-3 subset (orange) overall accuracy, with the Fruit-HSNet benchmark (70.73%) shown as a dashed line. (c, d) Spectral band importance (rolling mean

\pm

std) for ExtraTrees across 400–1000 nm; shaded regions highlight biologically meaningful zones (pigment changes, red-edge chlorophyll absorption, NIR cellular/water signatures). Ripeness importance concentrates in visible wavelengths (c), while firmness importance shifts toward NIR features (d).

Fruit ( $n$ )	ExtraTrees	RandomForest	LightGBM	HistGradBoosting	XGBoost
Avocado (170)	67.9	65.4	44.4	48.1	38.3
Kaki (68)	41.7	41.7	33.3	41.7	41.7
Kiwi (162)	58.3	54.2	62.5	70.8	50.0
Mango (68)	66.7	75.0	41.7	66.7	50.0
Papaya (51)	33.3	33.3	33.3	33.3	44.4

Table 6: Per-fruit ripeness accuracy (%) for the top-five models (Phase 4 single-split holdout,

n

=138; ranked by Phase 3 overall accuracy). Bold = best per fruit; underline = second-best.

Task	Class	Precision	Recall	F1
Ripeness	Overripe	0.561	0.696	0.621
Ripeness	Perfect	0.569	0.638	0.602
Ripeness	Unripe ∗	1.000	0.471	0.640
Firmness group		Ripeness accuracy
	Soft ( $n$ =27)	0.741
	Medium ( $n$ =68)	0.544

Table 7: ExtraTrees Phase 4 single-split holdout analysis (

n

=138). Top : ripeness per-class metrics from the confusion matrix (Fig. 7 a). Bottom : ripeness accuracy stratified by firmness group (4-bin analysis on the original unbalanced split). Extreme-firmness groups achieve higher ripeness accuracy than intermediate groups. ∗ Unripe precision (1.000) with 47.1% recall means the model rarely predicts unripe; most unripe fruit are misclassified as perfect (41.2%) or overripe (11.8%).

Figure 7: Classification patterns for the best model (ExtraTrees, single-split holdout,

n

=138). (a) Normalized ripeness confusion matrix showing primary confusion between adjacent maturation states. (b) Ripeness accuracy stratified by firmness group (4-bin); extreme-firmness groups achieve higher ripeness accuracy than intermediate groups.

Figure 8: Cross-validation stability (Phase 4, 10-fold stratified CV). (a) Overall accuracy with 95% confidence intervals (mean

\pm

t_{9,0.025}

\cdot

std, where

t_{9,0.025}\approx 2.262

) for all 20 models, sorted by mean. (b) Task-separated view: ripeness vs firmness accuracy with CIs. Firmness predictions are consistently more accurate and stable.

Strategy	Data State	Ripe $\uparrow$	Firm $\uparrow$	OA $\uparrow$	$\Delta$ ET
Hard Voting	Original	52.9	85.5	69.2	-5.8
Soft Voting	Original	50.0	84.8	67.4	-7.6
Stacking	Original	31.2	75.4	53.3	-21.7
Blending	Original	49.3	72.5	60.9	-14.1
Hard Voting	Strat. Resplit	57.2	84.1	70.7	-4.3
Soft Voting	Strat. Resplit	58.0	83.3	70.7	-4.3

Table 8: Ensemble performance comparison. All ensembles use the top-5 models from Phase 3.

\Delta

ET shows difference from best single model (ExtraTrees, 75.0%).

Figure 9: Ensemble performance decomposition by task. All eight ensemble configurations maintain high firmness accuracy (

>

72%) but catastrophically degrade ripeness accuracy (stacking drops to 31.2%). Dashed line: ExtraTrees single-model baseline (75.0%); dotted line: Fruit-HSNet benchmark (70.73%).

Figure 10: Feature group ablation: relative performance drop (%) when each spectral transformation group (224 bands) is removed. (a) Ripeness: first derivatives are most critical for LightGBM (8.3% drop); other groups are redundant or harmful for ExtraTrees. (b) Firmness: continuum-removed features dominate ExtraTrees (16.6% drop), while LightGBM shows uniform sensitivity across groups (

\leq

5.0%). Positive values indicate removing the group hurts performance; negative values indicate removal improves it. Task- and model-dependent contributions explain why PCA, which compresses all groups uniformly, degrades classification.

Figure 11: Preprocessing gain vs optimization gain for all 20 models. Each model’s accuracy improvement is decomposed into preprocessing gain (best Phase 2 config minus Phase 1 baseline, teal) and optimization gain (Phase 3 minus best Phase 2, purple). Models sorted by preprocessing gain. Dotted lines indicate means. For most models, preprocessing contributes the dominant share of improvement; optimization gain is near-zero or negative for many.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 1: Critical difference diagram from Nemenyi post-hoc test (

\alpha=0.05

) on 20-model

\times

10-fold overall F1 scores. Models connected by a horizontal bar are not significantly different. Models colored by algorithm family.

Figure 2: (a) Per-fold overall F1 score distributions from 10-fold stratified cross-validation, sorted by mean F1. Box plots show median and interquartile range; individual fold scores are overlaid as jittered points. Dashed line marks the Fruit-HSNet benchmark (70.7%). (b) Per-model Cohen’s

d

effect sizes for the HSI vs. VIS-3 subset modality comparison, with 95% confidence intervals. Shaded bands indicate negligible (

|d|<0.2

), small (

0.2

–

0.5

), and medium (

0.5

–

0.8

) effect regions. Models colored by algorithm family.

Figure 3: Per-fruit overall accuracy for the top-five models (Phase 4). Avocado and mango show highest performance; papaya consistently underperforms due to smallest sample size (

n

=51). Performance variation across species highlights deployment considerations for multi-fruit sorting systems.

Abbr.	Model	HP	FI	Lib.	Optuna Search Space
ET	Extra Trees	5	✓	sklearn	n_est $\in$ [50,500], depth $\in$ [3,30], split $\in$ [2,20], leaf $\in$ [1,20], crit $\in$ {gini,ent}
RF	Random Forest	6	✓	sklearn	n_est $\in$ [50,500], depth $\in$ [3,30], split $\in$ [2,20], leaf $\in$ [1,20], feat $\in$ {sqrt,log2, $\emptyset$ }, crit $\in$ {gini,ent}
DT	Decision Tree	4	✓	sklearn	depth $\in$ [3,30], split $\in$ [2,20], leaf $\in$ [1,20], crit $\in$ {gini,ent}
XGB	XGBoost	9	✓	xgboost	n_est $\in$ [50,500], lr $\in$ [.01,.3], depth $\in$ [3,10], child_w $\in$ [1,10], $\gamma$ $\in$ [0,1], sub $\in$ [.5,1], col $\in$ [.5,1], $\alpha$ $\in$ [0,1], $\lambda$ $\in$ [0,1]
LGBM	LightGBM	8	✓	lightgbm	n_est $\in$ [50,500], lr $\in$ [.01,.3], depth $\in$ [3,15], leaves $\in$ [10,300], child $\in$ [5,100], $\alpha$ $\in$ [0,1], $\lambda$ $\in$ [0,1], col $\in$ [.5,1]
GBC	Gradient Boosting	5	✓	sklearn	n_est $\in$ [50,500], lr $\in$ [.01,.3], depth $\in$ [3,10], split $\in$ [2,20], leaf $\in$ [1,20]

Appendix

Table 1: Overview of 20 evaluated models with Optuna hyperparameter search spaces. HP = number of tuned hyperparameters; FI = native feature importance. All ranges define the Bayesian optimization bounds (100 trials per model).

Rk	Model	Best Hyperparameters
1	ExtraTrees	n_estimators=277, max_depth=14, min_samples_split=6, min_samples_leaf=1, criterion=gini
2	RandomForest	n_estimators=285, max_depth=10, min_samples_split=5, min_samples_leaf=2, max_features=log2, criterion=entropy
3	LGBM	n_estimators=470, learning_rate=0.2874, max_depth=14, num_leaves=10, min_child_samples=90, reg_alpha=0.2373, reg_lambda=0.4189, colsample_bytree=0.6112
4	HistGradientBoosting	learning_rate=0.1795, max_iter=123, max_depth=5, min_samples_leaf=41, l2_regularization=0.5475
5	XGBoost	n_estimators=400, learning_rate=0.1357, max_depth=10, min_child_weight=8, gamma=0.1722, subsample=0.7181, colsample_bytree=0.5824, reg_alpha=0.3973, reg_lambda=0.5746
6	KNeighbors	n_neighbors=3, weights=distance, algorithm=ball_tree, p=1

Appendix

Table 2: Best hyperparameters found by Optuna (100 Bayesian TPE trials per model). Parameters listed after stripping the classifier__ prefix. Models ranked by Phase 3 overall accuracy.

Hardware
Component	Specification
Processor	Apple M4 (10-core CPU: 4 performance + 6 efficiency)
Memory	24 GB unified LPDDR5X
Storage	SSD
GPU	None (CPU-only training)
Software Environment

Appendix

Table 3: Hardware and Software Environment Specifications. All 20 models were trained on CPU only; no GPU acceleration was used.

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Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabijańska

1Efrei Research Lab, Paris Panthéon-Assas University, Paris, France · Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

Agricultural Image Analysis Hyperspectral Image Classification

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Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques. However, challenges including the limited availability of labeled data and the lack of robust methods generalizable to various hyperspectral cameras and fruit types can compromise the effectiveness of hyperspectral image-based FRP. Addressing these challenges, this paper introduces Fruit-HSNet, a machine learning architecture specifically designed for hyperspectral classification of fruit ripeness. Fruit-HSNet incorporates a spatio-spectral feature extraction module based on Fourier Transform and central pixel spectral signature followed by learnable feature fusion and a classifier optimized for ripeness classification. The proposed architecture was evaluated using the DeepHS Fruit dataset, the largest publicly available labeled real-world hyperspectral dataset for predicting fruit ripeness, which includes five different types of fruits-avocado, kiwi, mango, kaki, and papaya-captured with three distinct hyperspectral cameras at various stages of ripeness. Experimental results highlight that Fruit-HSNet substantially outperforms existing deep learning methods, from baseline to state-of-the-art models, with improvements of 12%, achieving a new state-of-the-art overall accuracy of 70.73%.

Abstract

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