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Open Access

ARTICLE

A Dual-Detection Method for Cashew Ripeness and Anthrax Based on YOLOv11-NSDDil

Ran Liu, Yawen Chen, Dong Yang*, Jingjing Yang*
School of Information Science and Engineering, Hebei North University, Zhangjiakou, 075000, China
* Corresponding Author: Dong Yang. Email: email; Jingjing Yang. Email: email
(This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070734

Received 22 July 2025; Accepted 15 October 2025; Published online 18 November 2025

Abstract

In the field of smart agriculture, accurate and efficient object detection technology is crucial for automated crop management. A particularly challenging task in this domain is small object detection, such as the identification of immature fruits or early stage disease spots. These objects pose significant difficulties due to their small pixel coverage, limited feature information, substantial scale variations, and high susceptibility to complex background interference. These challenges frequently result in inadequate accuracy and robustness in current detection models. This study addresses two critical needs in the cashew cultivation industry—fruit maturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model. The method introduces three key technological innovations: (1) The SDDil module is designed and integrated into the backbone network. This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost, effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects. Simultaneously, the SD module dynamically enhances discriminative features and suppresses background noise, significantly improving the model’s feature discrimination capability in complex environments; (2) The introduction of the DynamicScalSeq-Zoom_cat neck network, significantly improving multi-scale feature fusion; and (3) The optimization of the Minimum Point Distance Intersection over Union (MPDIoU) loss function, which enhances bounding box localization accuracy by minimizing vertex distance. Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model: mAP50 reaches 0.825, a 4.6% increase compared to the original YOLOv11; mAP50-95 improves to 0.624, a 6.5% increase; and recall rises to 0.777, a 2.4% increase. This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.

Keywords

Deep learning; object detection; multi-scale fusion; small object detection; miss detection; false detection

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