Developing thermal infrared de-ghost and multi-level nested conglutinated segmentation algorithm for detection of rice seed setting rate

Zhou, Jun; Lu, Xiangyu; Yang, Rui; Wang, Yaliang; Chen, Huizhe; Shen, Jianxun; Zhou, Zhenjiang; Liu, Fei

doi:10.1016/j.compag.2023.107725

Cited by 5 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, deep learning has been widely applied in agriculture. Convolutional neural network technology has been applied in the actual agricultural environment, such as image classification [26][27][28], object detection [29][30][31][32], and image segmentation [33][34][35][36][37], with good performance. Therefore, the study of peanut kernels and peanut pods in this field has attracted the attention of many scholars.…”

Section: Introductionmentioning

confidence: 99%

Method of Peanut Pod Quality Detection Based on Improved ResNet

Wang

et al. 2023

Agriculture

View full text Add to dashboard Cite

Peanuts are prone to insect damage, breakage, germination, mildew, and other defects, which makes the quality of peanuts uneven. The difference in peanut pod quality makes the price and economic benefit also have a big difference. The classification of peanut pods according to quality is an important part of improving the product grade and market competitiveness. Real-time, accurate, and non-destructive quality detection of peanut pods can effectively improve the utilization and commercial value of peanuts. The strong subjectivity of manual detection and the low efficiency and low accuracy of mechanical detection have caused considerable wastage. Therefore, the present study proposed a new convolutional neural network for the peanut pod quality detection algorithm (PQDA) based on an improved ResNet. Compared to previous models, this model is more practical with high accuracy, lightweight, and easy nesting. Firstly, the detection and classification effects of ResNet18, AlexNet, and VGG16 are compared, and ResNet18 was determined to be the best backbone feature extraction network for model training. Secondly, three models were designed to optimize and improve the algorithm. The KRSNet module was added to the algorithm to make the model lightweight. The CSPNet module was added to the algorithm to improve the learning efficiency of each feature layer. The Convolutional Block Attention Module (CBAM) was added to the algorithm to improve its ability to capture more feature information about peanut pods. The experimental ablation results show that the precision of the improved model PQDA reaches 98.1%, and the size of parameters is only 32.63 M. Finally, the optimized model was applied to other peanut pod varieties for generalization experiments, and the accuracy reached 89.6% and 90.0%, indicating the effectiveness of the proposed peanut pod quality detection model. Furthermore, the model is suitable for deployment on embedded resource-limited devices, such as mobile terminals, to achieve the real-time and accurate detection of peanut pod quality.

show abstract