A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Recognition and Early Stage Detection of <em>Phytophthora</em> in a Crop Farm Using IoT

Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection

ChatGPT in the context of precision agriculture data analytics

In this study we argue that integrating ChatGPT into the data processing pipeline of automated sensors in precision agriculture has the potential to bring several benefits and enhance various aspects of modern farming practices. Policy makers often face a barrier when they need to get informed about the situation in vast agricultural fields to reach to decisions. They depend on the close collaboration between agricultural experts in the field, data analysts, and technology providers to create interdisciplinary teams that cannot always be secured on demand or establish effective communication across these diverse domains to respond in real-time. In this work we argue that the speech recognition input modality of ChatGPT provides a more intuitive and natural way for policy makers to interact with the database of the server of an agricultural data processing system to which a large, dispersed network of automated insect traps and sensors probes reports. The large language models map the speech input to text, allowing the user to form its own version of unconstrained verbal query, raising the barrier of having to learn and adapt oneself to a specific data analytics software. The output of the language model can interact through Python code and Pandas with the entire database, visualize the results and use speech synthesis to engage the user in an iterative and refining discussion related to the data. We show three ways of how ChatGPT can interact with the database of the remote server to which a dispersed network of different modalities (optical counters, vibration recordings, pictures, and video), report. We examine the potential and the validity of the response of ChatGPT in analyzing, and interpreting agricultural data, providing real time insights and recommendations to stakeholdersComment: 33 pages, 21 figure

The Use of Computer Vision to Combat Losses from Disease in Grapevines

The use of computer vision to support and automate agriculture and viticulture is increasing. Therefore, it is important to continuously test new technologies and equipment. Management of pests and diseases in viticulture is a labour-intensive task. This study aims to investigate current technologies in computer vision that could be applied to disease and pest detection in viticulture and the application of transfer learning on segmentation networks. This study also implements a case study and applies computer vision for disease and pest detection. Observation of limitations in the network's performance on testing images, after training on the limited data set, suggests that careful control is needed over lighting conditions in the image capture environment. Although initial results are positive, a larger training dataset is recommended to achieve a greater level of accuracy. Keywords: Artificial Intelligence, Computer Visions, Viticulture, Sensors DOI: 10.7176/CEIS/14-3-01 Publication date:August 31st 202

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Ecology & computer audition: applications of audio technology to monitor organisms and environment

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition – a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence – is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states. We distinguish between applications related to organisms, such as species richness analysis and plant health monitoring, and applications related to the environment, such as melting ice monitoring or wildfire detection. This work positions computer audition in relation to alternative approaches by discussing methodological strengths and limitations, as well as ethical aspects. We conclude with an urgent call to action to the research community for a greater involvement of audio intelligence methodology in future ecosystem monitoring approaches

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page

Applications Of Machine Learning in Predicting Crop Yields for Sustainable Agriculture

Modern agriculture is increasingly adopting data-driven techniques to enhance productivity and sustainability. This comprehensive framework begins with Data Collection and Preprocessing, involving the meticulous sourcing of data from various channels and the critical processes of ensuring Data Quality and Cleaning. Machine Learning Models, such as Regression Models (including Linear Regression, Random Forest Regression, and Support Vector Machines), Time Series Analysis, and Deep Learning Models, play a pivotal role in predicting crop yields. These models are valuable tools that empower farmers and stakeholders to make informed decisions, optimize resource allocation, and respond to the ever-evolving challenges in agriculture. In this context, Predictive Features are harnessed, including Weather Data, Soil Quality and Composition, Pest and Disease Data, and Remote Sensing and Satellite Imagery. These features provide a holistic understanding of the factors that influence crop yields and enable the adoption of sustainable practices. However, the process is not without its Challenges and Considerations, encompassing data quality, model selection, local variability, interpretability, and adaptation to climate change. The Benefits of Yield Prediction in Sustainable Agriculture are extensive and include optimized resource management, early pest and disease control, sustainable land use, climate resilience, and data-driven decision-making. This data-driven approach supports the critical mission of ensuring food security, conserving resources, and building resilient agricultural systems for the future. Yield prediction is a transformative approach that not only increases agricultural productivity but also fosters sustainability and resilience in agricultur

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

IoT-Enabled Smart Robotic System for Greenhouse Management using Deep Learning Model with STS Approach

A significant component of any country's Gross Domestic Product is made up of farming and agriculture. Utilizing IoT in agriculture and farming methods is essential as the global population is projected to reach around 9.6 billion by 2050. To meet such high demand, an improvisation and optimization of the current farming technologies is the need of the hour. Numerous researchers developed different application specific system for agriculture but less attention was paid towards critical aspects such as intelligence, modularity and human centric design. There is lacuna in existing developed system in the utilization of advanced technologies to their full potential. The agricultural sector wants autonomous systems that are smarter and more effective. Therefore, this research paper introduced the smart solution as an intelligent modular autonomous system with human-centric design approach for agricultural application. The developed system able to detect plant disease with more than 96% accuracy with the help of novel deep learning model designed with sharpening to Smoothening approach. The disease detection and classification results has been verified through confusion matrix method of evaluation. An intelligent robotic system has been developed to detect plant diseases using novel deep learning model and perform multiple functions like greenhouse monitoring, pesticide sprinkling etc. The robotic system has control over internet through web control system so that farmer can monitor greenhouse and control robot activity from remote place. This smart farming solution able to make farmer life simpler and perform difficult task like plant disease detection and pesticide sprinkling easily