A method for detecting the quality of cotton seeds based on an improved ResNet50 model

Abstract

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning

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Quantifying Soybean Defects: A Computational Approach to Seed Classification Using Deep Learning Techniques

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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

“…applied machine vision technology with the YOLOV5 framework to detect damaged and mold-infested cottonseeds with over 99% accuracy. Du et al (2023) harnessed machine vision with the ResNet50 architecture for damaged cottonseed identification, reaching a 97.23% accuracy. For variety detection, Soares et al (2016) employed near-infrared hyperspectral imaging to classify cottonseed varieties with 91.7% accuracy.…”