Abstract
The use of digital systems in precision agriculture is becoming more and more attractive for farmers at every level. A few years ago, the use of these technologies was limited to large farms, due to the considerable income needed to amortize the large investment required. Although this technology has now become more affordable, there is a lack of scientific data directed to demonstrate how these systems are able to determine quantifiable advantages for farmers. Thus, the transition towards precision agriculture is still very slow. This issue is not just negatively affecting the agriculture economy, but it is also slowing down potential environmental benefits that may result from it. The starting point of precision agriculture can be considered as the introduction of satellite tractor guidance. For instance, with semi-automatic and automatic tractor guidance, farmers can profit from more accuracy and higher machine performance during several farm operations such as plowing, harrowing, sowing, and fertilising. The goal of this study is to compare semi-automatic guidance with manual guidance in wheat sowing, evaluating parameters such as machine performance, seed supply and operational costs of both the configurations.
Highlights
Today, transformation towards digital agriculture represents the new frontier of agricultural production, and it is recognized as the most valid solution to increasing the quality and sustainability of future food production worldwide [1,2,3]
A smart farming approach, by contrast, aims at minimizing the use of resources by intervening only where needed [6]. This approach is known as Precision Agriculture (PA), in other words, a discipline gathering electronic, computer and mechanical technologies in order to maximize agricultural production in a sustainable and efficient way [7]
The goal of this paper is to present a comparative field study concerning semi-automatic and manual guidance approaches, throughout the sowing of winter wheat, focusing on both technical and economic aspects
Summary
Transformation towards digital agriculture represents the new frontier of agricultural production, and it is recognized as the most valid solution to increasing the quality and sustainability of future food production worldwide [1,2,3]. Digital agriculture entails the transition from a conventional agriculture concept to a smart farming approach. A smart farming approach, by contrast, aims at minimizing the use of resources by intervening only where needed [6]. This approach is known as Precision Agriculture (PA), in other words, a discipline gathering electronic, computer and mechanical technologies in order to maximize agricultural production in a sustainable and efficient way [7]. The match between mechanical and electronic technologies is considered a key factor for success in many sectors on land and beyond [8]; common issues concern how these technologies will be spread and how fast the transition will occur. In order to provide an accurate application of precision farming, it is necessary to make inferences on the basis of collected and processed data, availing experts in the area [9]
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