It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.

Summarization systems for various applications, such as opinion mining, online news services, and answering questions, have attracted increasing attention in recent years. These tasks are complicated, and a classic representation using bag-of-words does not adequately meet the comprehensive needs of applications that rely on sentence extraction. In this paper, we focus on representing sentences as continuous vectors as a basis for measuring relevance between user needs and candidate sentences in source documents. Embedding models based on distributed vector representations are often used in the summarization community because, through cosine similarity, they simplify sentence relevance when comparing two sentences or a sentence/query and a document. However, the vector-based embedding models do not typically account for the salience of a sentence, and this is a very necessary part of document summarization. To incorporate sentence salience, we developed a model, called CCTSenEmb, that learns latent discriminative Gaussian topics in the embedding space and extended the new framework by seamlessly incorporating both topic and sentence embedding into one summarization system. To facilitate the semantic coherence between sentences in the framework of prediction-based tasks for sentence embedding, the CCTSenEmb further considers the associations between neighboring sentences. As a result, this novel sentence embedding framework combines sentence representations, word-based content, and topic assignments to predict the representation of the next sentence. A series of experiments with the DUC datasets validate CCTSenEmb's efficacy in document summarization in a query-focused extraction-based setting and an unsupervised ILP-based setting.