test(CropLens): smoke-test all nine bundled crop checkpoints on CPU

.github/workflows/real-yolo-smoke.yml

@@ -15,7 +15,7 @@ permissions:
   contents: read
 
 jobs:
-  wheat-sample:
+  all-crop-samples:
     runs-on: ubuntu-latest
     timeout-minutes: 20
     steps:
@@ -27,5 +27,5 @@ jobs:
         run: python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
       - name: Install Ultralytics
         run: python -m pip install ultralytics
-      - name: Run a real sample prediction on CPU
+      - name: Run real crop checkpoints on CPU
         run: python inference/tests/smoke_real_model.py

docs/TESTING.md

@@ -6,7 +6,7 @@
 | Vue build | `cd frontend && npm ci && npm run build` | The frontend bundle compiles | Browser integration or API responses |
 | Spring Boot checks | `cd backend && mvn -Dtest=DeepSeekControllerTest,FileControllerTest package` | Backend compilation, selected input/control tests | Database integration or paid AI connectivity |
 | Documentation links | `python scripts/check_docs_links.py` | Relative Markdown links resolve | External link availability |
-| **Real YOLO smoke test** | GitHub Actions: `Real YOLO CPU smoke test` | Loads an actual wheat checkpoint and predicts one bundled JPG on a GitHub-hosted CPU | Diagnostic accuracy, full application workflow, multi-user concurrency, camera/video streaming, other weights |
+| **Real YOLO smoke test** | GitHub Actions: `Real YOLO CPU smoke test` | Loads all nine crop checkpoints and predicts one corresponding bundled JPG per crop on a GitHub-hosted CPU | Diagnostic accuracy, full application workflow, multi-user concurrency, camera/video streaming, disease classification accuracy |
 
 ## Run the real inference smoke test
 
@@ -19,7 +19,7 @@ gh run list -R FuTseYi/CropLens --workflow real-yolo-smoke.yml --limit 1
 
 Alternatively, use **Actions → Real YOLO CPU smoke test → Run workflow** in the GitHub repository.
 
-The optional test installs CPU PyTorch/Ultralytics packages and may download substantial dependencies. It is intentionally not run on every PR or on ordinary documentation updates. Read the run logs for the actual pass/fail status. A passing smoke test indicates only that **one checkpoint can load and process one repository sample**; it says nothing about inference accuracy or deployment readiness.
+The optional test covers nine weight files and matching sample folders. It installs CPU PyTorch/Ultralytics packages and may download substantial dependencies. It is intentionally not run on every PR or on ordinary documentation updates. Read the run logs for the actual pass/fail status. A passing smoke test indicates only that **each tested checkpoint can load and process one matching repository sample**; it says nothing about inference accuracy or deployment readiness.
 
 ## Before claiming production readiness
 

inference/tests/smoke_real_model.py

@@ -1,39 +1,56 @@
-"""Execute one real CPU-only crop model prediction without Flask or a database.
+"""CPU-only smoke test: load and run each bundled crop model on one real JPG.
 
-Unlike the standard mock-based unit tests, this must load a checked-in
-Ultralytics checkpoint and decode an actual sample image. It does not
-evaluate diagnostic accuracy or full frontend/backend integration.
+No Flask, database or browser is needed. This checks basic compatibility,
+not prediction accuracy, safety or reliable crop-disease diagnosis.
 """
+import gc
 from pathlib import Path
+
 from ultralytics import YOLO
 
 
+CROPS = (
+    ("apple", "苹果"),
+    ("corn", "玉米"),
+    ("cotton", "棉花"),
+    ("grape", "葡萄"),
+    ("potato", "马铃薯"),
+    ("rice", "水稻"),
+    ("strawberry", "草莓"),
+    ("tomato", "番茄"),
+    ("wheat", "小麦"),
+)
+
+
 def main():
     root = Path(__file__).resolve().parents[2]
-    weights = root / "inference" / "weights" / "wheat_best.pt"
-    images = sorted((root / "assets" / "samples" / "小麦").glob("*.jpg"))
-    if not weights.is_file() or not images:
-        raise SystemExit("Required wheat checkpoint/sample not found")
-    image = images[0]
-
-    print("Model:", weights.relative_to(root))
-    print("Image:", image.relative_to(root))
-    model = YOLO(str(weights))
-    results = model.predict(
-        source=str(image),
-        device="cpu",
-        imgsz=640,
-        conf=0.25,
-        save=False,
-        verbose=False,
-    )
-    assert len(results) == 1, "Expected exactly one image prediction"
-    result = results[0]
-    assert result.boxes is not None, "Expected a detection result structure"
-    assert len(result.orig_shape) == 2 and all(n > 0 for n in result.orig_shape)
-    print("Real inference succeeded; image shape:", result.orig_shape)
-    print("Detections:", len(result.boxes))
-    print("This is a smoke test, not an accuracy or clinical evaluation.")
+    tested = 0
+    for crop, chinese_folder in CROPS:
+        weights = root / "inference" / "weights" / (crop + "_best.pt")
+        samples = sorted((root / "assets" / "samples" / chinese_folder).glob("*.jpg"))
+        if not weights.is_file() or not samples:
+            raise SystemExit("Missing checkpoint or sample for {}".format(crop))
+
+        image = samples[0]
+        print("Checking {} with {} ...".format(crop, image.relative_to(root)), flush=True)
+        model = YOLO(str(weights))
+        results = model.predict(
+            source=str(image), device="cpu", imgsz=640,
+            conf=0.25, save=False, verbose=False,
+        )
+        assert len(results) == 1, "Expected one image result for {}".format(crop)
+        result = results[0]
+        assert result.boxes is not None, "Missing detection result for {}".format(crop)
+        assert len(result.orig_shape) == 2 and all(n > 0 for n in result.orig_shape)
+        print("OK: {} — image {} — {} detections".format(
+            crop, result.orig_shape, len(result.boxes)), flush=True)
+        tested += 1
+        del result, results, model
+        gc.collect()
+
+    assert tested == len(CROPS)
+    print("All {} crop checkpoints completed CPU inference.".format(tested))
+    print("This is a smoke test only, NOT a validation of model accuracy.")
 
 
 if __name__ == "__main__":