Employing Drones in Agriculture: An Exploration of Various Drone Types and Key Advantages

This article explores the use of drones in agriculture and discusses the various types of drones employed for different agricultural applications. Drones, also known as unmanned aerial vehicles (UAVs), offer numerous advantages in farming practices. They provide real-time and high-resolution data collection, enabling farmers to make informed irrigation, fertilization, and pest management decisions. Drones assist in precision spraying and application of agricultural inputs, minimizing chemical wastage and optimizing resource utilization. They offer accessibility to inaccessible areas, reduce manual labor, and provide cost savings and increased operational efficiency. Drones also play a crucial role in mapping and surveying agricultural fields, aiding crop planning and resource allocation. However, challenges such as regulations and limited flight time need to be addressed. The advantages of using drones in agriculture include precision agriculture, cost and time savings, improved data collection and analysis, enhanced crop management, accessibility and flexibility, environmental sustainability, and increased safety for farmers. Overall, drones have the potential to revolutionize farming practices, leading to increased efficiency, productivity, and sustainability in agriculture.

Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review

In recent years, the popularity and use of Artificial Intelligence (AI) and large investments on theInternet of Medical Things (IoMT) will be common to use products such as smart socks, smartpants, and smart shirts. These products are known as Smart Textile or E-textile, which has theability to monitor and collect signals that our body emits. These signals make it possible to extractanomalous components using Machine Learning (ML) techniques that play an essential role in thisarea. This study presents a Systematic Review of the Literature (SLR) on Anomaly Detection usingML techniques in Smart Shirt. The objectives of the SLR are: (i) to identify what type of anomalythe smart shirt; (ii) what ML techniques are being used; (iii) which datasets are being used; (iv)identify smart shirt or signal acquisition devices; (v) list the performance metrics used to evaluatethe ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are beingapplied.The SLR selected 11 primary studies published between 2017-2021. The results showed that6 types of anomalies were identified, with the Fall anomaly being the most cited. The Support VectorMachines (SVM) algorithm is most used. Most of the primary studies used public or private datasets.The Hexoskin smart shirt was most cited. The most used metric performance was Accuracy. Onaverage, almost all primary studies presented a result above 90%, and all primary studies used theSupervisioned type of ML.