With the development of smart agriculture, the accumulation of data in the field of pesticide regulation has a certain scale. The pesticide transaction data collected by the Pesticide National Data Center alone produces more than 10 million records daily. However, due to the backward technical means, the existing pesticide supervision data lack deep mining and usage. The Apriori algorithm is one of the classic algorithms in association rule mining, but it needs to traverse the transaction database multiple times, which will cause an extra IO burden. Spark is an emerging big data parallel computing framework with advantages such as memory computing and flexible distributed data sets. Compared with the Hadoop MapReduce computing framework, IO performance was greatly improved. Therefore, this paper proposed an improved Apriori algorithm based on Spark framework, ICAMA. The MapReduce process was used to support the candidate set and then to generate the candidate set. After experimental comparison, when the data volume exceeds 250 Mb, the performance of Spark-based Apriori algorithm was 20% higher than that of the traditional Hadoop-based Apriori algorithm, and with the increase of data volume, the performance improvement was more obvious.
Greenhouse is an important part of facility agriculture and a typical application scenario of modern agricultural technology. The greenhouse environment has the characteristics of nonlinearity, strong coupling, large inertia, and multiple disturbances. There are many environmental factors and it is a typical complex system [7]. In smart greenhouses, control commands are mostly triggered by complex events with multi-dimensional information. In this paper, by building the aggregation structure of complex events in the greenhouse, the technology is applied in the greenhouse as a whole. The core innovations of this paper are as follows: through the analysis of the information transmission process in the greenhouse, combined with the characteristics of the scene, a CEP information structure with predictive modules is formed, which is conducive to the popularization and application of CEP technology in the agricultural field. Pointed out the importance of extreme conditions in the prediction of the greenhouse environment for model evaluation. By improving the loss function in the machine learning algorithm, the prediction performance of a variety of algorithms under this condition has been improved. Applying CEP technology to intelligent greenhouse control scenarios, a set of practical complex event processing systems for greenhouse control has been formed.
The prediction of extreme greenhouse temperatures to which crops are susceptible is essential in the field of greenhouse planting. It can help avoid heat or freezing damage and economic losses. Therefore, it's important to develop models that can predict them accurately. Due to the lack of extreme temperature data in datasets, it is challenging for models to accurately predict it. In this paper, we propose an improved loss function, which is suitable for a variety of machine learning models. By increasing the weight of extreme temperature samples and reducing the possibility of misjudging extreme temperature as normal, the proposed loss function can enhance the prediction results in extreme situations. To verify the effectiveness of the proposed method, we implement the improved loss function in LightGBM, long short-term memory, and artificial neural network and conduct experiments on a real-world greenhouse dataset. The results show that the performance of models with the improved loss function is enhanced compared to the original models in extreme cases. The improved models can be used to guarantee the timely judgment of extreme temperatures in agricultural greenhouses, thereby preventing unnecessary losses caused by incorrect predictions.
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