Multistage AI Crop Inspection and Segmentation Framework (MACISF)
Crop quality assessment is one of the most critical components of modern agriculture, which directly influences yield optimization, post-harvest management, and food security. Conventional evaluation of field crops relies on manual inspection, which is time-consuming, subjective, and unscalable. This paper proposes the Multistage AI Crop Inspection and Segmentation Framework (MACISF), an automated multistage deep learning pipeline for fruit quality evaluation from visual features. The pipeline is comprised of three stages: (i) an EfficientNetV2-L backbone, fine-tuned with a three-stage progressive strategy, for crop type classification; (ii) seven species-specific EfficientNetV2-S classifiers for healthy-versusdefective detection; and (iii) a YOLOv8m module for defect localization. Trained and evaluated on a composite dataset spanning seven fruit species, MACISF achieves 99.23% crop type accuracy (macro F1-score 99.23%, macro AUC-ROC 0.993), 99.39% mean health state accuracy with 99.51% mean defective-class recall, and a projected grading mAP@0.5 of 92.3%. On crop type classification, the Stage 1 classifier is second only to InceptionV3 (by 0.15 percentage points) among seven models trained on the same data, and outperforms ResNet-50, Xception, EfficientNetB3, DenseNet-121, and MobileNetV2. Unlike these singletask baseline models, MACISF additionally delivers health-state assessment, defect severity grading, and a per-unit economic and nutritional evaluation. Results are reported on heldout test data from a controlled dataset, and field validation is left for future work.