This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

The amount of seasonings used during food preparation is quite important information for modern people to enable them to cook delicious dishes as well as to take care for their health. In this paper, we propose a near real-time automated system for measuring and recording the amount of seasonings used during food preparation. Our proposed system is equipped with two devices: electronic scales and a camera. Seasoning bottles are basically placed on the electronic scales in the proposed system, and the scales continually measure the total weight of the bottles placed on them. When a chef uses a certain seasoning, he/she first picks up the bottle containing it from the scales, then adds the seasoning to a dish, and then returns the bottle to the scales. In this process, the chef's picking and returning actions are monitored by the camera. The consumed amount of each seasoning is calculated as the difference in weight between before and after it is used. We evaluated the performance of the proposed system with experiments in 301 trials in actual food preparation performed by seven participants. The results revealed that our system successfully measured the consumption of seasonings in 60.1% of all the trials.

An encoder-decoder (Enc-Dec) model is one of the fundamental architectures in many computer vision applications. One desired property of a trained Enc-Dec model is to feasibly encode (and decode) diverse input patterns. Aiming to obtain such a model, in this paper, we propose a simple method called curiosity-guided fine-tuning (CurioFT), which puts more weight on uncommon input patterns without explicitly knowing their frequency. In an experiment, we evaluated CurioFT in a task of future frame generation with the CUHK Avenue dataset and found that it reduced the mean square error by 7.4% for anomalous scenes, 4.8% for common scenes, and 6.6% in total. Some other experiments with the UCSD dataset further supported the reasonability of the proposed method.