Latest Technology Automation Applications in Precision Agriculture

There have been many developments in the latest technology related to automation in agriculture. The developments in automation in this agriculture can be discussed in two parts as recommendations for precision agriculture-oriented, new technologies sensitive agriculture applications, automatic sowing and harvesting.

Automation in Precision Agricultural Field

A form of agriculture, also known as precision agriculture, is a farm management concept based on the application of different technologies to manage the spatial and temporal variability associated with all aspects of agricultural production. Its main goal is to improve both crop performance and environmental quality. It has confirmed the economic and environmental benefits achieved by applying precision farming methodologies. However, academic research and professional reports show that the adoption rate of these technologies is still low.

Moreover, rather than using precision agriculture as a complete concept, most of the reported practices are using these techniques to address specific needs or to fill important gaps in farmers’ knowledge. In addition, although agronomists played a leading role in PA development, engineers worked diligently to provide the technologies needed to implement PA practices. Engineering innovations for PA include the development of sensors, controls and remote sensing technologies.

Autonomous mobile robots can be used in a variety of field operations. They can be used to facilitate the capture and processing of large amounts of data and can provide the capabilities required to work not only at the individual plant level but also at the full field level. Autonomous platforms to be used for future sowing and seeding, weeding, exploration, fertilizer application, irrigation and harvesting are within this scope. The most widely used robotic technology in precision agriculture is vehicle orientation and automatic orientation systems. This is because economic benefits can be easily achieved without the need for the integration of additional components or decision support systems. However, other technologies, particularly those related to remote sensing, the development of sensors and controls, are also used by teams that combine agronomists and engineers.

Automation in the Insemination and Harvesting Field

With regard to the perspective of service robotics, studies have been conducted to evaluate the application of agricultural mechanization and its current technologies and limitations for large-scale purposes. The study shows an increasing level of technological progress in agricultural robots in field and crop mapping, soil sampling, mechanical seeders and harvesters. The same approach shows the future trends and possible adoption of automated agricultural machinery. The results of this study show that there is a growing interest in autonomous and semi-autonomous systems to reduce the highest workload operations: these are tillage, seeding and harvesting.

To support the incremental development of seeding and harvesting robots, new strategies are proposed to drive the autonomous mobile robot in relevant scenarios. It makes a small review of common techniques for robot navigation in greenhouses and suggests a methodology based on machine learning. Due to the importance of manipulation tasks in the insemination, planting and harvesting processes, the manipulator action strategy has also been discussed by several authors. However, they usually do not specify path planning algorithms; The most common approach is direct displacement of the end effector to the desired position with position-based control and visual feedback control.

Mission planning strategies have only been studied by a few researchers. Usually, the harvesting task is limited to picking one fruit, avoiding the planning required to collect the rest. Still, the problem can be viewed from two angles: the inclusion path planning to collect all fruits in a scene, or the time minimization to move from one fruit to another. Barrier detection and prevention has been studied by an equally few authors. In addition to road planning algorithms, obstacle recognition has added great complexity to the solution. Several approaches are based on obstacle detection with collision sensors in the final effector.

A review of the use of technologies for automated activities in greenhouses shows the increasing application of wireless systems for environmental measurements. Additionally, the study shows that a significant number of research studies are aimed at developing robotic systems for fruit collection and extraction. In addition, the research community has put a lot of effort into developing robust fruit recognition techniques; moreover, there is a great need to improve the picking capabilities of planting and harvesting robots in order to move into a commercial application.

A review of vision control techniques and their potential applications in fruit or vegetable harvesting robots Fruit identification and localization is the most common problem studied by the authors. Many approaches, such as the definition of fruit ripeness, rely on RGB cameras as well as color and shape recognition. Although more information can be obtained with this type of technology, less work is being done on multispectral lightning. The next level of complexity involves the application of stereo vision systems and LIDAR to calculate fruit position in a 3D space.

This goal can only be achieved through the diversification and specialization of robotic systems. In order to achieve better results in harvesting tasks, newer and more sensitive sensors are needed. Researchers are examining modern sensor systems used in semi or fully automated robotic harvesting. Their research shows how the integration of various types of technology and sensor fusion can increase sensitivity in fruit recognition and localization activities.

Robots have found their place in agriculture in recent years. This chapter deals with the main application areas (precision agriculture, greenhouse cultivation and seeding and harvesting), analyzes the air, ground and special robots used in these applications, and describes two research projects on precision agriculture and greenhouse cultivation. The main results of these sections are summarized below.

It summarizes two different projects dealing with the application of robots in agriculture. The RHEA project uses aerial robots to find weeds in the fields and ground robots to apply local treatments on them. In addition, the other project introduces ground and air robots in greenhouses to take measurements of environmental variables. A number of lessons need to be learned from these experiences, including the potential of robots as moving sensory and actuation systems, as well as unstructured navigation challenges, the power to collaborate with heterogeneous fleets, and the limitations imposed by the autonomy of robots.