Exploring GPS Data for Operational Analysis of Farm Machinery

Abstract

Global Positioning System (GPS) has made a great evolution in different aspects of modern agricultural sectors. Today, a growing number of crop producers are using GPS and other modern electronic and computer equipments to practice Site Specific Management (SSM) and precision agriculture. This technology has the potential in agricultural mechanization by providing farmers with a sophisticated tool to measure yield on much smaller scales as well as precisely determination and automatic storing of variables such as field time, working area, machine travel distance and speed, fuel consumption and yield information. This study focuses on how to interpret and process raw GPS data for operational analysis of farm machinery. Exact determinations of field activities using GPS data along with accurate measurements and records of yield provide an integrated tool to calculate field efficiency and field machine index which in turn increases machine productivity and labor saving. The results can also provide graphical tools for visualizing machine operator's performance as well as making decision on field and machine size and selection.

SIMULATION OF EFFICIENCY IN AVAILABILITY FOR DIFFERENT SUGARCANE (Saccharum spp.) TRANSPORT EQUIPMENT

“…Production time is exclusively for productive work, that is, when the equipment is effectively performing the agricultural operation. In this context, according to Alizadeh (2011), Araldi et al (2013), Griffel et al (2020), Grisso et al (2002), Linhares et al (2012), Mohamed et al (2011), Oduma et al (2019), Pitla et al (2014), Santos et al (2018b), Shamshiri & Ismail (2013), and Zhou et al (2015), the operational times of service result in the field efficiency (Eff), which corresponds, in this study, to the availability efficiency (Efa) of the equipment of the sugarcane transport system. Efa comprises the worked hours that the equipment effectively performs its productive function, the auxiliary hours that are required according to the operation that the equipment necessarily needs for its full use (Banchi & Lopez, 2007).…”

Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

“…They found that GPS could accurately record the field location and the operation time of agriculture machinery [6]. In a similar study, a yield monitoring system was proposed to efficiently collect the GPS data and perform operational analysis of the farm machinery [33]. To analyse the agriculture machinery operational cost, Sopegno et al have developed a smart web and mobile platform called AMACA (Agricultural Machine App Cost Analysis).…”

Optimization method for accurate positioning seeding based on sowing decision

“…He et al [20] developed a sowing lag compensation algorithm, which can obtain a shorter lag distance. As can be seen from that summary of the literature, during decision-based operations, GPS positioning accuracy [21] , decision interpretation time and GPS information period are the main reasons for system time delay. At the same time, GPS positioning accuracy and grid positioning discrimination accuracy are the main reasons that affect the analytical value.…”