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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

In this paper, the performance of a new decision-directed signal reconstruction (DDSR) algorithm with optimal reconstruction thresholds for use with optical orthogonal frequency division multiplexing (OFDM) modulation is investigated. Clipping noise remains one of the main drawbacks in many practical optical OFDM transmission systems. It has been shown in previous studies that a time-domain based DDSR algorithm can be used to effectively reduce the clipping noise. However, in all existing works studying this DDSR algorithm, the decision threshold used to direct the time-domain signal reconstruction is fixed and equal to the level at which the transmitted signal is clipped. In this paper, it is shown that, due to the influences of the unavoidable noise at the receiver, fixing the DDSR threshold at the clipping level is not an optimal solution and can severely limit the advantages of using this algorithm. A detailed theoretical analysis is performed to investigate the key factors that influence the choices of the optimal DDSR thresholds. The presented simulation results are used to verify the accuracy of the theoretical analysis and also show that the performance of the DDSR algorithm is significantly improved by using the obtained optimal thresholds.