Sustainable Management Strategies and Biological Control in Apple Orchards

Sustainable horticultural practices address the global issues of food security, pest and disease management, soil health, water pollution, depletion of biodiversity, etc. with environment–friendly approaches. Increasingly, the adoption of such strategies is benefitting agricultural production including that in orchards. Even though several Integrated Pest Management (IPM), disease, and weed management strategies have been in use for the control of pests, diseases, and weeds in apple orchards, they are still not the most favored methods of control. There are various economic and acceptance concerns regarding their use, particularly in developing nations. A more sustainable system for apple orchards management, thus, should be adopted.   Here, we review various management methodologies, including the sustainable biocontrol methods, employed in the apple orchards. Reviewing these methods, we draw attention towards integrating sustainable IPM methodologies with biocontrol strategies like the use of pest-resistant cultivars, employing natural parasites and enemies of apple pests, use of agro-based pesticides, integration of technological advances that can provide real-time data to farmers and orchard scouting leading to the development of sustainable management of apple orchards. Such systems will not only reduce dependence on chemical control methods but will also minimize ecotoxicity. We also draw parallels from the biocontrol methods adopted in sustainable agri-production in other fruit orchards to suggest strategies that can be employed for sustainable apple production

Intelligent Orchard Monitoring: An IoT-Based Approach for Real-Time Apple Disease Detection Using Environmental Factors

This research introduces a novel methodology for apple disease detection based on environmental factors, integrating the capabilities of the Internet of Things (IoT). By deploying advanced sensors in orchards, the aim is to facilitate real-time monitoring and transform these spaces into intelligent ecosystems. The methodology encompasses data collection from environmental variables like temperature, humidity, pressure, and light. Using the Mamdani fuzzy inference system (MFIS), the collected data is then employed to predict potential apple diseases. Initial tests conducted in an apple orchard in Shimla, India, demonstrated the system's effectiveness and efficiency, with minimal delays during various phases of the process. The study also offers a comparative analysis with existing state-of-the-art methodologies in the realm of disease detection

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review

Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community

Research on organic agriculture in the Netherlands : organisation, methodology and results

Chapters: 1. Organic agriculture in the Netherlands; 2. Dutch research on organic agriculture: approaches and characteristics; 3. Dutch knowledge infrastructure for organic agricultur'; 4. Sustainable systems; 5. Good soil: a good start; 6. Robust varieties and vigorous propagation material; 7. Prevention and control of weeds, pests and diseases; 8. Health and welfare of organic livestock; 9. Animal production and feeding; 10. Special branches: organic greenhouse production, bulbs, ornamentals and aquaculture; 11. Healthfulness and quality of products; 12. Economy, market and chain; 13. People and society. A publication of Wageningen UR and Louis Bolk Institut

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

A data platform for real-time monitoring and analysis of the brown marmorated stink bug in Northern Italy

The brown marmorated stink bug (Halyomorpha halys) is one of the main insect pest species causing economic damage to several agricultural commodities worldwide and one of the worst threats to tree fruit crops in northern Italy, especially in the Emilia-Romagna region. Previous efforts in implementing H. halys surveillance at the regional level were mainly focused on studying the H. halys phenology, but they were not designed to provide a public service. In this paper, we propose a data-driven approach to support the application of Integrated Pest Management strategies against H. halys. The proposal is based on the experience of a three-year project in which a network of monitoring traps has been deployed throughout the whole Emilia-Romagna region and a data platform has been implemented to enable the real-time tracking of H. halys occurrence and distribution, integrating these information with multiple data sources, and analytical capabilities through a public website. Besides the real-time pest surveillance, the data platform allowed us to increase our understanding about H. halys seasonal invasion dynamics and the main factors contributing to its spread. The results will help individual growers in protecting their crops and the whole region in promoting more efficient usage of insecticides and more sustainable and healthy agricultural productions

Pathogen Population Biology Research can Reduce International Threats to Tree Health Posed by Invasive Fungi

Humankind owes much to trees, given their major role in sequestering carbon and providing oxygen, sugars and much of the energy on which ourselves and terrestrial ecosystems depend. Trees and forests are important culturally, economically, environmentally and socially. And yet, despite this, trees throughout the world are currently facing an increasing number of serious challenges. On a global scale, Curtis et al. (2018) report most forest loss is due to commodity driven deforestation through permanent conversion to nonforest land uses including agriculture (e.g. palm oil production), energy production and mining. The other main drivers of global forest loss, that might instead be considered less permanent and associated with subsequent regrowth, include forestry, shifting agriculture and wildfire. Additional threats to forests include those posed by climate change, and invasive biotic agents such as insect pests and pathogens. Most new tree disease outbreaks are due to introduction events, with a potential pathogen introduced from their endemic centres of origin (where they generally cause little or no disease on their plant host due to long-term coevolution) into a new geographic location, in which a naive host has not previously been exposed and can thus be highly susceptible. The incidence of such 'new encounter' diseases is increasing at an unprecedented rate due to globalisation with increased international trade in plants and travel, a scenario potentially exacerbated by a changing climate better suited to establishment of a pathogen once introduced. Identification of the centres of origin of fungal pathogens can be important for several reasons. First, given that the original host-fungus interaction will have typically stabilised over long periods of time, such geographic regions could be useful sources of host genetic resistance. Moreover, the longer time periods involved will have likely resulted in greater genetic diversity accruing in such endemic populations. More diverse pathogen populations have greater evolutionary potential, with increased genetic variation available for response to environmental change. This could enable host tolerance to be overcome, unexpected 'jumps' onto new hosts, increased risk of fungicide resistance, and better adaptability to changing environmental conditions (e.g. temperature). Thus, strategies to reduce introduction of additional genetic variation from source to sink regions can reduce tree health threats. In this article, such introduction events are considered in the context of three devastating tree diseases, namely ash dieback, Dutch Elm Disease (DED) and Dothistroma Needle Blight (DNB, mainly on pine). On all these tree hosts, multiple closely-related fungal species have now been associated with each of these different diseases. Such related species are often morphologically very similar or even indistinguishable by eye, and consequently this can result in taxonomic confusion and species misidentification, leading to delayed diagnosis of the true causal agent of a given disease outbreak. Research into such related species is important as they might pose very different plant health threats that require distinct disease management strategies. These differences might relate to pathogenicity, geographic distribution, host range, effectiveness of host resistance, sensitivity to fungicides, temperature optima, reproductive strategy and so on. Furthermore, when related fungal species come into physical contact with each other after a long period of separation, for instance via an introduction event, various outcomes are possible including: (1) replacement (and possible extinction) of one species by the other; (2) coexistence of the species; or (3) cross-species hybridisation. The remainder of this article focuses, using three major tree disease case histories, on how fundamental research on pathogen biology can provide new insights into the genetic structure of related pathogen populations that can be usefully applied to reduce the threat to tree health posed by invasive fungal species