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In recent years, food problems have arisen due to population changes. To solve this problem, Advanced technologies such as robots and artificial intelligence are increasingly being used to improve the efficiency of agriculture. In particular, plant factories are attracting attention because they have a high affinity for advanced technologies and can be produced regardless of the cultivation location and climate. However, production in plant factories exhibits of higher management costs and lower profitability than traditional cultivation methods. It is thought that this problem can be solved by predicting plant growth and notifying the farm manager. In this research, we will use data that can be measured at plant factories to create a machine learning model which predicts, both the size and weight of an agricultural product from a single piece of data. As a result, we were able to predict multiple items using a relatively lightweight model. The overall error was small, with an average error rate of about 15%. Although the average error rate for weight was about 30%, we were able to create a model that behaves close to the actual measured values. [ABSTRACT FROM AUTHOR]

In this paper, we report on the high image quality, large data capacity, and low computational cost of a distributed data embedding watermarking scheme that uses a Max-plus algebra-based Morphological wavelet Transform (MMT). Previously, we evaluated the basic MMT watermarking scheme for image quality and robustness against image compression attacks defined by the Information Hiding and its Criteria (IHC). The proposed distributed data embedding scheme focuses on middle frequency signal-groups after the MMT decomposition process for embedding watermarks. During the experiments for evaluating scheme capability, we processed 12 benchmark images using the proposed watermarking scheme. The proposed method records to increase the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM) values. For example, the PSNR value of the middle frequency is improved by 6 dB (24 to 30 dB) higher than that of the high-frequency value. The proposed scheme can keep 30 dB for embedding 12 bits of the watermark in the transformed image of all benchmark images. The SSIM value of the middle frequency is about 1.15 (0.88 to 0.98) times higher than the high-frequency value. Thus, all benchmark images of the SSIM values achieve up to 0.5 when the watermark data of 24-bit are embedded using the proposed distributed scheme. The processing performance on the mobile processor is approximately equal to previous studies despite better image quality. The distributed data embedding scheme indicates the possibility of obtaining practical results compared with related studies. [ABSTRACT FROM AUTHOR]

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