Wireless sensor networks (WSN) usually have limited energy and transmission capacity, which can't match the transmission of a large number of data collected by sensor nodes. So, it is necessary to perform in-network data compression in the WSN. This paper proposes an algorithm of data compression based on multiple principal component analysis (multiple-PCA), iteratively using PCA method in multiple layers. Theoretically and experimentally, the proposed algorithm can efficiently remove the correlation between the raw sensor measurements and also that between the principal components (PC) of the neighboring cluster heads, and efficiently improve the data compression ratio under the premise of ensuring the data reconstruction accuracy, thus better reduce the energy consumption of sensor nodes.

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Instance segmentation of fruit tree canopies from images acquired by unmanned aerial vehicles (UAVs) is of significance for the precise management of orchards. Although deep learning methods have been widely used in the fields of feature extraction and classification, there are still phenomena of complex data and strong dependence on software performances. This paper proposes a deep learning-based instance segmentation method of litchi trees, which has a simple structure and lower requirements for data form. Considering that deep learning models require a large amount of training data, a labor-friendly semi-auto method for image annotation is introduced. The introduction of this method allows for a significant improvement in the efficiency of data pre-processing. Facing the high requirement of a deep learning method for computing resources, a partition-based method is presented for the segmentation of high-resolution digital orthophoto maps (DOMs). Citrus data is added to the training set to alleviate the lack of diversity of the original litchi dataset. The average precision (AP) is selected to evaluate the metric of the proposed model. The results show that with the help of training with the litchi-citrus datasets, the best AP on the test set reaches 96.25%.

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