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The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high resource languages such as French, German, and Spanish. In this paper we address this gap by tackling offensive language identification in Marathi, a lowresource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy.MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID [1].

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Soil is important to humans and all living things on earth because it acts as the root source for agriculture, food and medicine. Soils are of different types and each soil type can have different composition of minerals, humus, organic matter and can hold different characteristics based on which different crops can be grown. So we need to know the features and characteristics of various kinds of soils of different places to understand which crops grow better in certain soil types in different climatic conditions and what kind of fertilizers/ pesticides can be added to make the crop grow healthily. So we've proposed a system which can be a great use to the farmers and common public to predict what kind of crops can be grown for different types of soil series based on agricultural data analysis using machine learning techniques and image processing Keywords: Soil, Crop, Agriculture, crop recommendation, soil classification, machine learning