Fruit Ripeness Classification using CNN

Fruit packhouses and retailers grade ripeness by eye — a slow, subjective job where the same banana can be called ripe by one grader and overripe by another, and the verdict shifts across graders and shifts. An automated grader promises consistency, but the task is genuinely hard: visually adjacent stages overlap in color cues (ripe versus overripe is a continuum, not a line), and lighting, background and camera variation change what the photo shows. A photograph also cannot measure sugar, acidity or firmness — so the system must be honest about being appearance-based grading only, never a certified quality instrument. This project studies the idea end to end, training a multi-stage CNN classifier on public banana and mango ripeness image collections with an explicit scope: the model separates unripe, ripe and overripe stages per fruit, and the notebook's confusion matrix documents the real error pattern — which adjacent stages the model confuses — openly in the report.

Dataset & model:

Dataset name: Public banana and mango ripeness image collections (open research photo sets labeled by ripeness stage).

Source: Openly available research image sets for banana and mango ripeness.

Task: Multi-class image classification (appearance-based ripeness grading).

Classes: Unripe / ripe / overripe per fruit — the stage scheme defined by the build's labeling over the public collections (appearance cues only: color and texture).

Model: CNN classifier — custom convolutional backbone, plus an ImageNet-pretrained transfer-learning variant for comparison (TensorFlow/Keras).

Input: Fruit photographs, normalized to a fixed input size with background-aware cropping.

Prediction: Probability distribution over the ripeness stages.

Output: Most likely ripeness stage with a confidence score; confusion matrix documenting confused adjacent-stage pairs.

Evaluation metrics: Accuracy, per-class precision, recall, F1 and confusion matrix on a held-out validation split — computed by the Jupyter notebook during the buyer's build. No measured performance claimed; design target approximately 85% or better accuracy on the split.

Working:

1. Fruit photographs are loaded from the public banana and mango ripeness collections, normalized to a fixed input size and background-aware cropped.
2. Preprocessing: augmentation with rotations, flips, brightness and hue jitter tolerates the lighting, background and camera variation of real fruit photos.
3. Training phase: a CNN (custom backbone, plus an optional transfer-learning variant) trains for multi-stage ripeness classification, with accuracy and loss logged each epoch.
4. Evaluation phase: the Jupyter notebook runs the full evaluation on the held-out validation split — accuracy, per-class precision, recall, F1, confusion matrix — and collects misclassified photos into a review gallery.
5. Inference phase: the trained model scores new fruit photos, outputting the most likely ripeness stage with a confidence score.
6. Output: per-photo ripeness stage prediction with confidence; the confusion matrix documents exactly which adjacent stages the model confuses.

Specifications:

Model | CNN classifier (custom backbone + ImageNet-pretrained transfer-learning variant), TensorFlow/Keras

Dataset | Public banana and mango ripeness image collections (open research sets)

Task | Multi-stage appearance-based ripeness classification

Classes | Unripe / ripe / overripe per fruit (appearance cues: color, texture)

Input | Fruit photographs, fixed input size with background-aware cropping

Output | Most likely ripeness stage with confidence score

Evaluation | Accuracy, per-class precision, recall, F1, confusion matrix on held-out validation split — computed during the buyer's build

Design target | Approximately 85% or better accuracy on the split (target, not a measured claim)

Error analysis | Confusion matrix + misclassified-photo gallery exposing confused adjacent-stage pairs

Scope | Appearance-based grading only

• [Multi-Stage Ripeness Grading] (implemented) — CNN classifier grading unripe, ripe and overripe stages per fruit from appearance cues.
• [Per-Photo Stage Prediction] (implemented) — The most likely ripeness stage with a confidence score and an adjustable threshold.
• [From-Scratch vs Transfer-Learning Comparison] (implemented) — A custom convolutional backbone and an ImageNet-pretrained transfer-learning variant are both included for direct comparison.
• [Color-Tuned Augmentation] (implemented) — Rotations, flips, brightness and hue jitter tuned for color-based grading under varied lighting.
• [Adjacent-Stage Error Analysis] (implemented) — Confusion matrix plus misclassified-photo review exposes exactly which adjacent stages the model confuses.
• [Background-Aware Cropping] (implemented) — Fruit regions are cropped with background awareness before classification.
• [Additional Fruits or Stages] (optional) — New fruits or finer stage granularity can be added with additional labeled photos and a retraining run, scoped at quotation.

• Complete source code (data pipeline, CNN training, transfer-learning variant, inference script)
• Jupyter evaluation notebook (validation metrics, confusion matrix, misclassification review)
• Project report PDF (background, datasets, methodology, evaluation, error analysis)
• PPT presentation for final review
• Viva Q&A preparation document (CNNs, image classification, transfer learning, metrics, grading context)
• Setup guide (environment, dataset download, training and inference commands)

• Scope is strictly appearance-based grading: the system cannot measure sugar, acidity or firmness, and must never be presented as a certified quality instrument — no lab-test equivalence is claimed.
• Adjacent stages (ripe vs overripe) overlap in color cues and are the known confusion pairs; the confusion matrix documents the real error pattern.
• Lighting, background and camera variation outside the training data classify worse.
• The model only knows bananas and mangoes in the stages represented in the public collections; other fruits and unseen varieties will not grade correctly.
• This is a design-target-driven research prototype evaluated on public datasets — not a packhouse-certified grading system.