Task-based agricultural mobile robots in arable farming: A review

In agriculture (in the context of this paper, the terms “agriculture” and “farming” refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farming includes temporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting where robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farmin

Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

Visual teach and generalise (VTAG)—Exploiting perceptual aliasing for scalable autonomous robotic navigation in horticultural environments

Nowadays, most agricultural robots rely on precise and expensive localisation, typically based on global navigation satellite systems (GNSS) and real-time kinematic (RTK) receivers. Unfortunately, the precision of GNSS localisation significantly decreases in environments where the signal paths between the receiver and the satellites are obstructed. This precision hampers deployments of these robots in, e.g., polytunnels or forests. An attractive alternative to GNSS is vision-based localisation and navigation. However, perceptual aliasing and landmark deficiency, typical for agricultural environments, cause traditional image processing techniques, such as feature matching, to fail. We propose an approach for an affordable pure vision-based navigation system which is not only robust to perceptual aliasing, but it actually exploits the repetitiveness of agricultural environments. Our system extends the classic concept of visual teach and repeat to visual teach and generalise (VTAG). Our teach and generalise method uses a deep learning-based image registration pipeline to register similar images through meaningful generalised representations obtained from different but similar areas. The proposed system uses only a low-cost uncalibrated monocular camera and the robot’s wheel odometry to produce heading corrections to traverse crop rows in polytunnels safely. We evaluate this method at our test farm and at a commercial farm on three different robotic platforms where an operator teaches only a single crop row. With all platforms, the method successfully navigates the majority of rows with most interventions required at the end of the rows, where the camera no longer has a view of any repeating landmarks such as poles, crop row tables or rows which have visually different features to that of the taught row. For one robot which was taught one row 25 m long our approach autonomously navigated the robot a total distance of over 3.5 km, reaching a teach-generalisation gain of 140

Development of New Cotton Defoliation Sprayer Using Unmanned Ground Vehicle and Pulse Width Modulation Technology

Chemical spraying is one of the most important and frequently performed intercultural agriculture operations. It is imperative to utilize appropriate spraying technology as a selection of ineffective one leads to waste of agrochemicals to the non‐target area. Several precision technologies have been developed in the past few decades, such as image processing based on real‐time variable‐rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton (Gossypium hirsutum L.) is an important industrial crop. It is a perennial crop with indeterminate growth habit; however, in most parts of the United States, it is grown as an annual crop and managed using growth regulators. Cotton defoliation is a natural physiological phenomenon, but untimely and/or inadequate defoliation by natural processes necessitates the application of chemical defoliants for efficient harvest. Defoliation is a major production practice influencing harvester efficiency, fiber trash content, cotton yield, and fiber quality. Currently, defoliant spraying is done by conventional ground driven boom sprayer or aerial applicator and both systems spray chemical vertically downwards into the canopy, which results in less chemical reaching the bottom of the canopy. Thus, a new autonomous ground sprayer was developed using robotics and pulse width modulation, which travels between two rows covering the whole canopy of the plant. Field research was conducted to evaluate the (i) effect of duty cycles (20%,40%, and 60%) on droplet characteristic (droplet distribution, deposition, and drift potential), defoliation cotton fiber and (ii) effect of duty cycles on cotton yield and II fiber quality. Droplet characteristics (droplet distribution, density, and potential droplet drift) were non-significant across the treatments and results from the water‐sensitive paper field test showed adequate penetration with low flow rates. Therefore, a 20% duty cycle was sufficient to defoliate based on the result of the field experiment. Likewise, the defoliants could be applied safely at the duty cycles tested without influencing fiber quality except for nep/gm, length (Ln), L (5%), short fiber content (SFCn), trash content in field 1 and micronaire, nep size, length (Ln), span length (5%), SFC, and fiber fineness in field 2 which were significant. However, the 20% duty cycle significantly reduced the amount of defoliant and would be a good choice for the autonomous cotton defoliation. This is a significant development as there is a huge potential to save on the cost of applying defoliant chemicals and the environment