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Abstract:Planning is widely been used in many areas such as in medicine, administration, business, logistics, education, environment and family matters. In automated planning research, the word "plans" refers specifically to plans of action. It is about the representation of future behavior, generally a series of action, with temporal and other constraints on them for execution by some agents. Theoretically, planning is an important part of rational behavior. In this study, we review some of the studies that have been conducted in menu planning. It covers the introduction of menu planning, the state of the arts for nutrition care expert systems and the approaches that have been applied. Furthermore, the advantages and limitations of the methods commonly used for menu planning are also highlighted in this study.

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

Fine-grained visual classification (FGVC) is challenging task due to discriminative feature representations. The attention-based methods show great potential for FGVC, which neglect that the deeply digging inter-layer feature relations have an impact on refining feature learning. Similarly, the associating cross-layer features methods achieve significant feature enhancement, which lost the long-distance dependencies between elements. However, most of the previous researches neglect that these two methods are mutually correlated to reinforce feature learning, which are independent of each other in related models. Thus, we adopt the respective advantages of the two methods to promote fine-gained feature representations. In this paper, we propose a novel CLNET network, which effectively applies attention mechanism and cross-layer features to obtain feature representations. Specifically, CL-NET consists of 1) adopting selfattention to capture long-rang dependencies for each element, 2) associating cross-layer features to reinforce feature learning,and 3) to cover more feature regions,we integrate attention-based operations between output and input. Experiments verify that CLNET yields new state-of-the-art performance in three widely used fine-grained benchmark datasets, including CUB-200-2011, Stanford Cars and FGVC-Aircraft. The url of our code is https://github.com/dlearing/CLNET.git. INDEX TERMSAssociating cross-layer features, attention-based operations, self-attention, CLNET.

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