A Survey on Machine Learning and Text Processing for Pesticides and Fertilizer Prediction - et. al., P.Pandiarajaa , (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
In the Agriculture sectors the pressure is increased immensely due to the rise in population. In this present year we mainly witness a move from conventional methods to the advanced technology with advent of the technology. Machine Learning and Text Processing have transformed the quality and the quantity of agriculture. The real time monitoring systems along with hybridization of species paved away for resource efficiency. Scientists and researchers across the globe are mainly shifting towards collaborative projects and ideologies to explore this field for saving society. The optimal solution has been provided by racing in the tech industry. This application will be portable, scalable and durable that provides help for initiating new areas in the agriculture field. So, this survey focuses on machine learning methodologies along with Text Processing systems in agriculture. This startup’s private and public sector which is around the world that provides smart and sustainable solutions are briefly discussed. Based on agriculture this scenario, limitations, applications, development and future parameters are also briefly explained. The greatest challenge for the future agriculture lies in making agriculture research and extension more demand driven and client oriented. In addition, laboratory analysis has been undertaken to assess the nitrogen status and the soil carbon of fresh paddy soils and the used paddy soils as well as saplings. During the first season of 2011 the data collected on pests, disease incidence and severity and yield are being compiled for analysis. Therefore, integrated research to develop a bottom up participatory technology extension approach using new technologies is suggested for sustainable development in agriculture.