Precision Farming and Predictive Analytics in Agriculture Context

Abstract

The scope of sensor networks and the Internet of Things spanning rapidly to diversified domains but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature as smart farming or precision farming. At its early stage of smart farming, the practices applying in agriculture farming are limited to collect the data related to the context of farming, such as soil state, weather state, weed state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices taken by the agriculturists depends on their experience. In this regard, the computer-aided predictive analytics by machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is reviewing the existing set of computer-aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation. It is imperative from the review of the literature that right from the farming process and techniques to usage of distinct sets of farming precision models like the machine learning solutions and other such factors indicate that there are potential ways in which the precision farming solutions can be resourceful for the farming groups. Optical sensing, soil analysis, imagery processing based analysis, machine learning models that can support in effective prediction are some of the key areas wherein the numbers of solutions that have offered from the market are high. From the compiled sources of literature in the study, there must be many techniques, tools, and available solutions, but one of the key areas wherein the solutions are turning complex for the companies is about usage of the comprehensive kind of machine learning models used in the precision farming which is currently a major gap and is potential scope for the future research process. This contemporary review indicating that both supervised and unsupervised machine learning models are yielding results, still in terms of improvements that are essential in precision farming. The overall efforts of this review portraying that, there is a need for developing a system that can self-train on the critical features based on the loop model of features gathered from the process and make use of such inputs for analysis. If such clustered solution is gathered, it can help in improving the quality of analysis based on the learning practices and the historical data captured from the systems aligned.