Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.

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Amharic is morphologically complex and under-resourced language, posing difficulties in the development of natural language processing applications. This paper presents the development of semantic role labeler for Amharic text using end-to-end deep neural network architecture. The system implicitly captures morphological, semantic and contextual features of a word at different levels of the architecture, and incorporates the syntactic structure of an input sentence. The proposed neural network architecture has four core layers from bottom to top, namely non-contextual word embedding, contextual word embedding, fully connected and sequence decoding layers. The non-contextual word embedding layer is formed from the concatenation of character-based, word-based and sentence-based word embeddings. This layer captures the morphological and semantic features of a given word by making use of BiLSTM recurrent neural network. At the contextual word embedding layer, a context sensitive embedding of a word is generated by applying a new LSTM layer on the top of the non-contextual concatenated word embedding layer. A fully connected network layer is added on top of contextual word embedding layer to supplement it by extracting dependencies among training samples in the corpus. At the sequence decoding layer, a sequence of semantic role labels is predicted using a linear-chain conditional random field algorithm by capturing the dependency among semantic role labels. In addition to the four core layers, the architecture has dropout layers to prevent overfitting problem. The proposed system achieves 94.96% accuracy and 81.2% F1 score when it is tested using test data.

In information retrieval (IR), documents that match the query are retrieved. Search engines usually conflate word variants into a common stem when indexing documents because queries and documents do not need to use exactly the same word variant for the documents to be relevant. Stemmers are known to be effective in many languages for IR. However, there are still languages where stemmers or morphological analyzers are missing; this is the case for Amharic which is the working language of Ethiopia. Morphological analysis is the key to derive stems, roots (primary lexical units) and grammatical markers of words such as person, tense and negation markers. This paper presents morphologically annotated Amharic lexicons as well as stem-based and root-based morphologically annotated corpora which could be used by the research community as benchmark collections either to evaluate morphological analyzers or information retrieval for Amharic. Such resources are believed to foster research in Amharic IR.