The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.
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.
Aiming at the problems of seedling injury and planting leakage due to the lack of seeding clamping force detection and real-time control in vegetable transplanting, a force feedback gripper was developed based on the linear Hall element. The mechanical properties of the stem of pepper cavity seedlings were first analyzed to provide a basis for the design of the gripper. A linear Hall sensor, a magnet, an elastic actuator, and an Arduino Uno development board make up the grasping force detecting system. Upon picking up a seedling, the elastic actuator, which is connected to the magnet, bends like a cantilever beam. As a result of the micro-displacement created by the elastic actuator, the Hall sensor’s voltage changes and can be used to determine the clamping force. Detection avoids direct contact between the sensor and the cavity seedlings, reducing the risk of sensor damage. Finite element method (FEM) simulations were used to determine the initial spacing between the magnet and Hall sensor and the effect of the elastic actuator. Control commands are sent to the servo based on the gripping force collected by the Arduino Uno board. Finally, the functions of accurate measurement, display, storage, and control of the clamping force of the cavity tray seedlings are realized, so that the damage rate of the cavity tray seedlings is reduced. In order to explore the influence of the elastic actuators on the clamping force detection system and the performance of the force feedback gripper, a calibration test of the clamping force detection system and a test of the indoor transplantation of pepper seedlings were carried out. Based on the calibration test, the clamping force detection system has a sensitivity of 0.0693 V/N, linearity of 3.21%, an average linear coefficient of determination of 0.986, and a range of 10 N, which fully meet the clamping force detection accuracy requirements during transplantation. Indoor tests showed that the force feedback gripper was stable and adaptable. This study can provide a reference for detecting and controlling clamping forces during transplantation.
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