Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.
In order to increase the power conversion efficiency and improve the power factor, a modified forward converter is proposed, which adopts a capacitive output filter instead of the inductive output filter of the conventional forward converter. Therefore, the proposed converter has wide input voltage range in opposite to that of the conventional forward converters.Moreover, the proposed converter uses the critical conduction mode for automatic current shaping to improve the power factor.As a result, the proposed converter can achieve quasi-resonant zero-voltage-switching, which can minimize the switching loss of main MOSFET. In addition, the operational principle of the proposed converter is analyzed and the characteristic of the proposed converter is investigated in this paper. To validate the effectiveness of the proposed converter, a prototype of 25W is implemented and the experimental results are discussed in more detail.
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