Artificial Intelligence-Driven Autonomous Robot for Precision Agriculture

Abstract

In the recent years, robotic systems became more advanced and more accessible. This has led to their slow, but stable integration and use in different processes and applications, including in the agricultural domain. Nowadays, agricultural robots are developed with the aim to replace the human labour in the otherwise exhausting, time-consuming or dangerous activities. Agricultural robotic systems provide many advantages, which can differ based on the type of the robot and its sensors, actuators and communication systems. This paper presents the design, the construction process, the main characteristics and the evaluation of a prototype of a small-scale agricultural robot that can be used for some of the simplest activities in agricultural enterprises. The robot is designed as an end-user autonomous mobile system, which is capable of self-localization and can map or inspect a specific farming area. The decision-making capabilities of the robot are based on artificial intelligence (AI) algorithms, which allow it to perform specific actions in accordance to the situation and the surrounding environment. The presented prototype is in its early development and evaluation stages and the paper concludes with discussions on the possible further improvements of the platform.

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

“…Participants are the basic perception modules in the MCS system. ey include userend smart mobile devices that handle the assigned perception tasks and upload the collected perception data in time [17]. e workflow of MCS is displayed in Figure 3.…”

A Review of Robots, Perception, and Tasks in Precision Agriculture

“…Precision Agriculture, which comprises the application of contemporary information and communication technologies to boost agricultural yields and profitability, has received a lot of attention in recent years [7]. Precision Agriculture minimises the number of resources needed to produce fields, such as arable land, water, fertilisers, and other agrochemicals [8]. The most popular definition of PA is given by Pierce and Nowak [9]: "Precision Agriculture is the application of farming strategies and methodologies to do the right thing, in the right place and at the right time", while data and technologies detect and decide what is right [10].…”

“…As a result, autonomous navigation relies on the proper integration of a localisation system, such as the GNSS, with a collection of exteroceptive sensors (e.g., image-based systems, sonars, radars, LIDARs, and so on) to locate and map at the same time. By analysing the information offered by these solutions before they can provide agronomic advice or instructions, the post-processing and mapping of the data may provide farmers with knowledge of important resources, as well as information on how much and when they should be used [8]. This is the heart of PA, and it needs an in-depth understanding of the obtained sensory data in order to appropriately analyse it and determine the needed actions.…”

Una revisión sobre el rol de la inteligencia artificial en la industria de la construcción

“…Por otro lado Beloev et al (13) indican que la inteligencia artificial tiene un ámbito muy amplio, por ello va diseñadas para reflejar las características asociadas con el comportamiento humano para la inferencia y la autocorrección a un ritmo exponencial, por lo que se espera que con el tiempo aprenda reglas simples para aplicaciones como la detección de fraudes o la elaboración de perfiles de riesgo.…”