Precision agriculture using IoT data analytics and machine learning

TLDR

Agricultural science has become increasingly data‑driven and precise, and the rise of IoT technologies has transformed the sector, offering new opportunities while also presenting challenges. The study aims to demonstrate how IoT‑enabled data analytics and machine learning can improve crop production, exemplified by an apple‑disease prediction model for Kashmir orchards, while also addressing integration challenges. The authors built an IoT‑based sensor network to collect agricultural data, applied machine‑learning algorithms to predict apple disease in Kashmir orchards, and surveyed farmers to assess technology adoption and challenges.

Abstract

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including "smart agriculture or precision agriculture". Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches.