European stakeholders' perspectives on implementation potential of precision weed control: the case of autonomous vehicles with laser treatment.

Weed control is a basic agricultural practice, typically achieved through herbicides and mechanical weeders. Because of the negative environmental impacts of these tools, alternative solutions are being developed and adopted worldwide. Following recent technical developments, an autonomous laser-based weeding system (ALWS) now offers a possible solution for sustainable weed control. However, beyond recent proof of performance, little is known about the adoption potential of such a system. This study assesses the adoption potential of ALWS, using a mixed-method approach. First, six macro-environmental factors regarding the adoption of ALWS were determined. This assessment is referred to as a Political, Economic, Social, Technological, Legal, Environmental (PESTLE) analysis and is conducted in a form of a literature review initiated by expert consultations. Second, a range of European stakeholders' perceptions of ALWS was evaluated in four focus-group discussions (n = 55), using a strengths, weaknesses, opportunities, threats (SWOT) analysis. The factors identified in the PESTLE and SWOT analyses were subsequently merged to provide a comprehensive overview of the adoption potential of ALWS. Labour reduction, precision treatment and environmental sustainability were found to be the most important advantages of ALWS. High costs and performance uncertainty were identified as the main weaknesses. To promote the adoption of ALWS, this study recommends the following: (1) Concrete performance results, both technical and economic, should be communicated to farmers. (2) Farmers' knowledge of precision agriculture should be improved. (3) Advantage should be taken of policies that are favourable towards non-chemical methods and the high demand for organic products. This article also extensively discusses regulatory barriers, the risks posed to the safety of both humans and the machines involved, technological challenges and requirements, and policy recommendations related to ALWS adoption.

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset.

Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable.

A new approach to monitor and assess the damage caused by two-spotted spider mite.

This study uses an image-processing technique to determine the damage level caused by two-spotted spider mite (Tetranychus urticae Koch) to cucumber plants and changes in the number of mites in a greenhouse. Firstly, a new agricultural platform was developed to ensure camera stability for capturing quality images. The images of 50 leaves infested with T. urticae were captured weekly for 5 weeks with the platform, which resulted in 250 images. Fifty of these captured images were randomly selected and processed with an image-processing algorithm developed using an image processing toolbox module of MATLAB. The results obtained from the image processing algorithm were compared with expert observations. The image-processing method predicted the damage with 3.91 root mean squared error (RMSE). A highly significant positive relationship was found between image processing and expert observations. The results indicate that this new image-processing method may be successfully used in place of expert observation to determine T. urticae damage in greenhouses.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots.

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

A bibliometric and thematic approach to agriculture 4.0.

Researchers are developing digital solutions for agriculture. Humanity has perfected agriculture throughout history because this activity is fundamental to our existence. The agricultural sector is currently incorporating new technologies from other areas. This phenomenon is agriculture 4.0. However, a challenge to research is the integration of technologies from different knowledge fields, and this has caused theoretical and practical difficulties. Thus, our purpose with this study has been to understand the core agriculture 4.0 research themes. We have used a bibliometric analysis, and guided the data collection by the PRISMA protocol. VosViewer and Bibliometrix software generated the results. We found two main research fronts, one focussed on agriculture 4.0 development, and another on the impacts of agriculture 4.0, which may be positive or negative. We found 21 main keywords or topics researched in agriculture 4.0 related to these research fronts. These themes are within five different axes. We managed to establish a good understanding of the topics around agriculture 4.0. Future studies could focus on the responsible development of digital solutions for agriculture. This is because the social, environmental, and economic impacts of these new solutions may be positive or negative. We conclude that digital agriculture is the node technologies integration for the automation of agricultural activities.

Farm robots: ecological utopia or dystopia?

Farm robots may lead to an ecological utopia where swarms of small robots help in overcoming the yield penalties and labor requirements associated with agroecological farming - or a dystopia with large robots cultivating monocultures. Societal discussions and policy action are needed to harness the potential of robots to serve people and the planet.

Extraction of navigation line based on improved grayscale factor in corn field / Obtenção de linhas de tráfego em lavoura de milho baseada no fator de escala de cinza melhorado

ABSTRACT: Focusing on the problem that corn plant in different growth periods is grayed out by known methods, the gray scale difference of different part is large or the soil discrimination degree is not high, the navigation path is low in accuracy and speed. This paper proposed a new method for extracting cornfield navigation baselines, which is used to control walking of agricultural robots. Design method included image segmentation, navigation point extraction, and navigation path fitting. Image segmentation is based on a new grayscale factor combined with median filtering, OSTU method and morphological operations to achieve the separation of crops and soil. The extraction of the navigation point is based on the binary image vertical projection map to obtain the region of interest, and the navigation point coordinates are determined by calculating the relative center point of the white pixel points of the sampling line in the region of interest. The Hough transform is used to fit the navigation point obtained by the vertical projection map to determine the navigation path, and then the control parameters are obtained. The gray scale factor that is improved in this paper combined with the vertical projection map can extract the target ridge with an accuracy rate of 92%, and the accuracy of extracting the navigation line is more than 90%. When conducting navigation tracking experiments in corn field, the maximum error is 5cm.

RESUMO: Depois de usar o método conhecido como escala de cinza para plantas de milho, a diferença de escala de cinza entre diferentes partes da planta é grande ou a diferenciação do solo não é alta, a precisão do trajeto de navegação é baixa e a velocidade é lenta. Neste trabalho foi proposto um novo método de extração da linha de referência de navegação para campos de milho, que é usado para controlar a caminhada de robôs agrícolas. Os métodos de projeto incluem: segmentação de imagem, extração de ponto de navegação e encaixe de linha de navegação. A segmentação por imagem é baseada na separação de culturas e solos com base no novo fator de escala de cinza combinado com a filtragem mediana, método de Otsu e operação morfológica. A extração de pontos de navegação é baseada em um mapa de projeção vertical de imagem de dois valores para obter a área de interesse, e as coordenadas do ponto de navegação são determinadas por cálculo do ponto central relativo no pixel branco da linha amostral na área de interesse. O fator de escala de cinza melhorado nesta pesquisa irá extrair o centro da entre linha de plantio alvo com uma taxa de precisão de 92%. A precisão de extração da linha de navegação é mais de 90%. O erro máximo foi de 5cm quando o experimento de rastreamento de navegação em tempo foi é realizado em campos de milho.