Moisture measurement in crops using spherical robots

The purpose of this paper is to present a new low-cost system based on a spherical robot for performing moisture monitoring in precision agriculture. Design/methodology/approach – The work arose from the necessity of providing farmers with alternative methods for crop monitoring. Thus, after analysing the main requirements, a spherical robot was chosen as a tentative approach. The presented work summarizes the work carried out in selecting the basics to apply in the robot, as well as its mechanical and electronic design. After designing and constructing the robot, several tests have been performed, in order to validate the robot for performing monitoring task and moving on different types of soil. Findings – The performed tests reveal that spherical robot is a suitable solution for performing the task. Research limitations/implications – Some improvements in control should be applied in order to reach a fully autonomous navigation in very slippery soils. Nevertheless, the performance of the robot in teleoperated mode allows validating of the system. Practical implications – The robot turned out to be friendly and harmless in its use for this application. The cost of final series will be affordable in comparison with the cost of other methods. Endurance of the robot can be considered as fair. Originality/value – The paper presents a new tool for farming based on non-common robot

Non invasive moisture measurement in agricultural fiel using a rolling spherical robot

Irrigation management in large crop fields is a very important practice. Since the farm management costs and the crop results are directly connected with the environmental moisture, water control optimization is a critical factor for agricultural practices, as well as for the planet sustainability. Usually, the crop humidity is measured through the water stress index (WSI), using imagery acquired from satellites or airplanes. Nevertheless, these tools have a significant cost, lack from availability, and dependability from the weather. Other alternative is to recover to ground tools, such as ground vehicles and even static base stations. However, they have an outstanding impact in the farming process, since they can damage the cultivation and require more human effort. As a possible solution to these issues, a rolling ground robot have been designed and developed, enabling non-invasive measurements within crop fields. This paper addresses the spherical robot system applied to intra-crop moisture measurements. Furthermore, some experiments were carried out in an early stage corn field in order to build a geo-referenced WSI map

Robots in Agriculture: State of Art and Practical Experiences

The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming

Multisensory System for Fruit Harvesting Robots. Experimental Testing in Natural Scenarios and with Different Kinds of Crops

The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system

Advancing Near Surface Soil Moisture Measurements Using Robotics, Automation, and Remote Sensing

Near-surface soil moisture, or the water content within the soil, is important for understanding the interactions between land and the atmosphere, and for monitoring plants in agricultural settings. However, soil moisture can be highly variable within the same field and varies considerably with time. The challenge involved with measuring soil moisture is that traditional techniques that rely on obtaining large samples are labor and time-intensive, especially for large fields. Developments in sensor technologies have allowed users to record the soil moisture regularly at the points where the sensors are installed. However, to understand how soil moisture changes across a field from sensors installed at single points, there needs to be a large number of sensors installed, which are not easily moved and are expensive. Remote sensing approaches that use imagery from satellites and drones have been used to develop soil moisture prediction models. Developing these models requires measurements from the field to validate them. However, collecting data from large fields on a regular basis is challenging. Also, remote sensing models using machine learning techniques tend to be “black box models”, or models that do not reveal any information about their inner workings and may not have any physical significance to soil moisture. To address the challenges presented here, a first-of-its-kind, cost-effective fully autonomous drone payload was developed to measure near-surface soil moisture. A new validation technique for the payload sensor measurements was developed that only relies on two pieces of data–depth of insertion and sensor signal–to obtain a calibrated moisture content. Finally, a soil moisture prediction model was developed using the soil line concept, which is a linear relationship between bare soil reflectance observed in two different wavebands, combined with machine learning models to add physical meaning to the models. The three techniques developed in this dissertation address the challenges in near-surface soil moisture measurements and represent significant progress toward automating critical data collection across large fields in agriculture

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production