ข้อมูลจากบทคัดย่อ
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ด้านล่างเป็นบทคัดย่อต้นฉบับภาษาอังกฤษจากข้อมูลบรรณานุกรม โปรดตรวจบทความต้นฉบับก่อนอ้างอิง
Background and Aim: Mango (Mangifera indica) is a globally important fruit crop whose productivity is repeatedly threatened by foliar diseases such as anthracnose, bacterial canker, powdery mildew, die-back, and sooty mould. Although deep learning has rapidly advanced automated leaf-disease diagnosis, recently reported accuracies on the MangoLeafBD benchmark are approaching saturation, and many studies still rely on random data splits that may inflate performance through leakage of duplicate or near-duplicate images. This study aimed to develop and rigorously evaluate a leakage-aware, deployment-oriented deep learning framework for the complete eight-class MangoLeafBD task. Methods: Three modern transfer-learning backbones—EfficientNetB0, MobileNetV3Large, and ConvNeXtTiny—were fine-tuned using a two-stage training strategy on a group-aware data partition. Duplicate and near-duplicate images were detected with cleaned filename stems, average hashing (aHash), and difference hashing (dHash) and grouped before splitting to guarantee zero cross-partition overlap. The strongest models were combined through probability-level soft voting. Robustness was further assessed using 5-fold StratifiedGroupKFold cross-validation. Explainability was examined with Gradient-weighted Class Activation Mapping (Grad-CAM), deployment suitability was characterized through CPU latency benchmarking, and the framework was operationalized as a publicly accessible web-based diagnostic system. Results: EfficientNetB0 achieved the highest performance under the leakage-controlled protocol, reaching 99.50% accuracy and 99.50% weighted F1-score on the 599-image group-aware test set. The heterogeneous soft-voting ensemble matched EfficientNetB0 but did not exceed it, indicating that the ensemble gains reported in earlier studies may partly reflect optimistic split conditions. Five-fold grouped cross-validation confirmed stability, yielding a mean accuracy of 99.63% (SD = 0.20%) and a mean weighted F1 of 99.62% (SD = 0.20%). Grad-CAM visualizations showed that the model attended to biologically meaningful lesion regions across all eight classes, and CPU benchmarking produced a mean single-image latency of 25.3 ms (p95 = 27.6 ms) with batch throughput scaling up to 166.5 images per second. Conclusions: The proposed framework demonstrates that a compact, leakage-aware EfficientNetB0 model can match the accuracy of more complex hybrid CNN–transformer architectures while remaining interpretable and deployable on commodity CPU hardware. By coupling group-aware evaluation, explainable AI, latency benchmarking, and a publicly accessible web application, the study advances reproducible, deployment-ready precision agriculture research and offers a more rigorous benchmarking protocol for future mango leaf disease classification studies.
เหตุผลที่อยู่ในฐานติดตาม
ระเบียนนี้ได้รับ Impact Signal 77/100 จากความใหม่ แหล่งเผยแพร่ ความร่วมมือ และสัญญาณในข้อมูลบรรณานุกรม คะแนนนี้ใช้จัดลำดับการติดตาม ไม่ใช่การตัดสินคุณภาพงานวิจัย
ประเด็นที่เกี่ยวข้อง: Smart Agriculture and AI · Advanced Neural Network Applications · Remote Sensing in Agriculture
บทบาทของนักวิจัยและสถาบันไทย
Wirapong Chansanam · Suparp Kanyacome · Khon Kaen University · Kasetsart University · Sakon Nakhon Rajabhat University
ข้อจำกัดของข้อมูล
หน้านี้เป็นระเบียนบรรณานุกรมและข้อมูลจากบทคัดย่อ ยังไม่ใช่บทวิเคราะห์ฉบับเต็มหรือการประเมินคุณภาพงานวิจัย ควรตรวจสอบ DOI และเอกสารต้นฉบับก่อนนำไปอ้างอิง