v0.2.0 — Offline Android App & Coffee Diagnosis

Added

• Offline-first SQLite persistence (expo-sqlite, expo-crypto), repository layer for farms, plots, and scans with sync queue metadata, History screen with multi-filter and swipe-to-delete with undo, Scan Detail screen with farm/plot location assignment, and Farms/Plots CRUD screens (#26).
• Camera screen with viewfinder framing guide and shutter flash, crop-specific photo tips modal, analyzing sweep-line animation, and result screen with severity progress meter, confidence breakdown, pop-in status badge with haptics, low-confidence retake state, and agronomic disclaimer (#25).
• On-device TFLite inference pipeline (app/src/ml/) with expo-image-manipulator, jpeg-js RGB decoding, training normalization, react-native-fast-tflite, threshold calibration, and Jest cross-platform tensor parity test (#24).
• Onboarding (3-slide educational flow with mandatory agronomic disclaimer acceptance), Home (camera-first layout, primary Scan CTA, crop selector chips, offline status pill, and empty state), and Settings screens (instant es/en language switcher, light/dark theme toggle, model version, dataset licenses) (#23).
• Reusable UI component library in app/src/components/ui/ (Button, Card, Chip, Badge, Sheet, Toast, Skeleton, EmptyState, ProgressMeter) with spring micro-interactions, haptics, >= 48 dp touch targets, reduced motion support, and RNTL unit tests (#22).
• Agricultural design tokens (app/src/theme/) covering colors, typography, spacing, radii, elevation, and motion with dark/light themes and WCAG 2.1 AA mathematical contrast tests (#21).
• ThemeProvider and useTheme() hook integrating user theme preference with system appearance and reduced motion accessibility (#21).
• Expo SDK 52 Android application setup (app/app.json, app/package.json) with package com.keylorpineda.healthycrop, New Architecture, and Expo Router (#20).
• Internationalization subsystem (app/src/i18n/) with Spanish default, English secondary, symmetric translation keys, and mandatory agronomic disclaimer (#20).
• Zustand preferences store (app/src/stores/preferences.ts) managing locale, light/dark/system theme, and onboarding state (#20).
• Jest + React Native Testing Library configuration (jest-expo) with native module mocks and unit tests (#20).

v0.1.0 — Foundation & Coffee ML

Added

• Phase 0 repository foundation, monorepo workspace configuration (pnpm + uv), and .gitignore / .gitattributes / .editorconfig rules.
• GitHub Community Standards (LICENSE, CODE_OF_CONDUCT.md, CONTRIBUTING.md, SECURITY.md, SUPPORT.md, CITATION.cff, CODEOWNERS, issue templates, PR template, and dependabot.yml).
• Project documentation (AGENTS.md, docs/PLAN.md, docs/ARCHITECTURE.md, docs/DATASETS.md, docs/MODEL_CARD.md, docs/EXPERIMENTS.md, docs/FIELD_VALIDATION.md, docs/DEVLOG.md, and Spanish setup guide docs/SETUP_SERVICES_ES.md).
• CI and release GitHub Actions workflows (.github/workflows/ci.yml and .github/workflows/release.yml) and empty test suites for app/, api/, and ml/.
• Idempotent, SHA-256 verified coffee dataset downloaders (ml/scripts/download_rocole.py, ml/scripts/download_bracol.py) with truncated-ZIP recovery for BRACOL (#13).
• Coffee dataset preparation pipeline (ml/configs/coffee.yaml, ml/scripts/prepare_coffee.py, ml/src/healthycrop_ml/preprocess.py, dedup.py, split.py) performing EXIF orientation correction, 224x224 RGB conversion, perceptual-hash deduplication (imagehash.phash), and grouped stratified 70/15/15 splitting (#14).
• Coffee dataset report (docs/data_reports/coffee.md), 224x224 sample grid (docs/assets/data_reports/coffee_grid.png), and < 50 training-image merging evaluation (#15).
• Frozen-backbone MobileNetV3-Large baseline training pipeline (ml/src/healthycrop_ml/model.py, evaluate.py, train.py, ml/scripts/train_coffee.py, ml/notebooks/01_coffee_training.ipynb), confusion matrix (docs/assets/confusion_matrices/coffee_baseline.png), and baseline metrics (79.72% test accuracy, 0.7135 test Macro-F1) in docs/EXPERIMENTS.md (#16).
• Top-block MobileNetV3-Large fine-tuning, validation logit bias calibration (82.75% uncalibrated / 81.35% calibrated test accuracy, 0.8701 4-major-class F1), confusion matrix (docs/assets/confusion_matrices/coffee_finetuned.png), and per-class Grad-CAM visual explanations (ml/src/healthycrop_ml/gradcam.py, docs/assets/gradcam/coffee_gradcam.png) (#17).
• Float16 TFLite export pipeline (ml/src/healthycrop_ml/export_tflite.py, ml/scripts/export_coffee_tflite.py): 6.06 MB coffee model at 81.12% test accuracy (0.23 pp below Keras), validation-calibrated confidence threshold 0.79 (90.9% accepted precision), ml/models/coffee/registry.json, and the shared preprocessing fixture ml/fixtures/preprocessing_fixture_224.json (#18).
• Coffee model card (docs/MODEL_CARD.md §2): intended use, data, Keras vs TFLite metrics, per-class results with abstention coverage, Grad-CAM, and limitations; TFLite export results in docs/EXPERIMENTS.md (#19).