Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Study of new technological implications to improve food productivity and security in Ghana : case insights into the use of drones in cocoa farming

Since the early 1980’s, in developed countries such as Japan and the United States of America, several technological applications have been used experimentally to boost food production and enhance farming practices, especially in areas which are not geographically accessible for traditional farming practices and machineries.One such technology which has been extensively experimented with and deployed is the Unmanned Aerial Vehicle (UAV), which is an example of technological expertise pioneered by the military. Their growing adaptation in precision agriculture means that UAV have been used on farms in developed countries for crops grown on both small- and large land acreage for the purposes of identifying nutrient deficiencies, diseases, water and soil status, weeds, damage, and plant diagnostics.The study focuses on the adaptation and implementation of UAV in Ghana’s cocoa farming and the position of stakeholders in terms of their acceptance, as the country is currently the world’s second largest producer and exporter of cocoa. The study applies Disruptive Innovation theory and stakeholder theory as a joint conceptual framework by which to examine how new and long-established farms create, sustain, and continuously introduce creative and novel technology in order to maximise food production while assessing stakeholders’ attitudes and roles in the implementation of innovation.Conducted in Nkawie in the Ashanti region of Ghana, the study adopts a qualitative approach, using semi-structured interviews to elicit and collate the views of stakeholders on the implementation of UAV in cocoa farming in Ghana, ultimately analysing the resulting by use of NVivo software. The findings show that traditional practices and superstitious beliefs, lack of credit facilities can impede the acceptance of new innovation.The study identifies a comprehensive pool of stakeholders in the supply chain whose input significantly influences the implementation of UAV. Other key stakeholders maintained that limited support for local drone innovator community, access to funding, and corrupt practices hinder the implementation of this technology, although general awareness of its benefit to cocoa farming cannot be disputed. Despite the difficult conditions that arose during data collection due to COVID restrictions in the study area, 36 participant agreed to participate in the study through interviews. This study makes a specific contribution to the body of literature and policy framework on the drivers and barriers of UAV adoption and implementation in emerging economies such as Ghana in the cocoa farming industr

Protótipo e método para análise de cultivos no espectro visível e infravermelho a partir de um veículo aéreo multirotor

Plant health has a direct impact on the quality and quantity of agricultural products. Due to this fact, farmers must monitor crop conditions frequently. However, the current tools for achieving this are complex and inaccessible. Therefore, this article proposes a method for the characterization of crops that allows to monitor the plants using photographs in the visible and infrared spectrum acquired from a multi-rotor air vehicle, using low-cost cameras and free use tools for designing a prototype of processing information. The characterization is performed by identifying the normalized difference vegetation index (NDVI) in the photographic mosaics of the crops. This index provides information about plant health: Consequently, it is calculated and represented on a NDVI map, where the status of a crop is analyzed. The highest values of NDVI represent healthy plants, and the lowest do so for plants with problems, water, or others. The proposed  ethod allows the monitoring of crops in a temporary and spatial form, letting a producer to adopt measures that help the optimization of resources.La salud de las plantas tiene un impacto directo en la calidad y cantidad de los productos agrícolas. Debido   esto, los agricultores deben monitorear las condiciones de los cultivos con frecuencia, pero las herramientas actuales para llevar a cabo esta tarea son complejas e inaccesibles. Frente a esta situación, se propone en este artículo un método para la caracterización de cultivos que permita un monitoreo de las plantas a través fotografías en el espectro visible e infrarrojo adquiridas desde un vehículo aéreo multirrotor, mediante cámaras de bajo costo y herramientas de uso libre para el diseño de un prototipo de  rocesamiento de información. La caracterización se realizó mediante la identificación del índice de vegetación de diferencia normalizado (NDVI) en los mosaicos fotográficos de los cultivos. Este índice es capaz de proveer información acerca de la salud de las plantas, por lo cual se calcula y representa en un mapa NDVI en el que se analiza el estado del cultivo. Los valores más altos de NDVI representan a las plantas saludables, y los más bajos a las plantas con problemas, al agua u otros. El método propuesto permite monitorear cultivos de forma temporal y especial, con lo cual se llevaría al productor a tomar medidas que permitan la optimización de recursos. A saúde das plantas tem um impacto direto na qualidade e quantidade dos produtos agrícolas. Devido a isso, os agricultores devem monitorar as condições dos cultivos com frequência. Diante dessa situação, propõe-se neste artigo um método para a caracterização de cultivos que permita um monitoramento das plantas por meio de fotografias no espectro visível e infravermelho adquiridas a partir de um veículo aéreo multirotor, mediante câmeras de baixo custo e ferramentas de uso livre para o desenho de um protótipo de processamento de informação. A caracterização realizou-se mediante a identificação do índice de vegetação de diferença normalizada (NDVI) nos mosaicos fotográficos dos cultivos. Esse índice é capaz de prover informação a respeito da saúde das plantas, pelo qual se calcula e representa num mapa NDVI no qual se analisa o estado do cultivo. Os valores mais altos de NDVI representam as plantas saudáveis, e os mais baixos as plantas com problemas, a água ou outros. O método proposto permite monitorar cultivos de forma temporária e especial, com o qual se levaria o produtor a tomar medidas que permitam a otimização de recursos.&nbsp

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes

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

Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era