The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

This paper presents a new actuation mechanism to drive comb-drive actuators. An asymmetric configuration of the finger overlap was used to generate capacitive coupling for the actuation mechanism. When the driving voltages were applied on the stators, a voltage would be induced at the rotor due to the capacitive coupling. Then, an electrostatic force would be exerted onto the rotor due to the voltage differences between the stators and the rotor. The actuator’s static displacement and resonant frequency were theoretically analyzed. To verify the design, a comb-drive actuator with different initial finger overlaps, i.e., 159.3 μm and 48.9 μm on each side, was fabricated and tested. The results show that the actuator worked well using the proposed actuation mechanism. A static displacement of 41.7 μm and a resonant frequency of 577 Hz were achieved. Using the actuation mechanism, no electrical connection is required between the rotor and the outside power supply. This makes some comb-drive actuators containing heterogeneous structures easy to design and actuate.

In this study, we mechanically strengthened a borosilicate glass wafer by doubling its bending strength and simultaneously enhancing its transparency using surface nanostructures for different applications including sensors, displays and panels. A fabrication method that combines dry and wet etching is used for surface nanostructure fabrication. Specifically, we improved the bending strength of plain borosilicate glass by 96% using these surface nanostructures on both sides. Besides bending strength improvement, a limited optical transmittance enhancement of 3% was also observed in the visible light wavelength region (400–800 nm). Both strength and transparency were improved by using surface nanostructures of 500 nm depth on both sides of the borosilicate glass without affecting its bulk properties or the glass manufacturing process. Moreover, we observed comparatively smaller fragments during the breaking of the nanostructured glass, which is indicative of strengthening. The range for the nanostructure depth is defined for different applications with which improvements of the strength and transparency of borosilicate glass substrate are obtained.