Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(21), P. 4486 - 4486

Published: Nov. 8, 2021

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts and has been widely used where advanced automation control have required. The trend shifted from classical image processing techniques modern artificial intelligence (AI) deep (DL) methods. Based on large training datasets pre-trained models, DL-based methods proven be more accurate than previous traditional techniques. wide applications agriculture, detection weeds pests crops. Variation lighting conditions, failures transfer learning, object occlusion constitute key challenges this domain. Recently, DL gained much attention due its advantages detection, classification, feature extraction. algorithms can automatically extract information amounts data model complex problems is, therefore, suitable for detecting classifying We present a systematic review AI-based systems detect weeds, emphasizing recent trends DL. Various discussed clarify their overall potential, usefulness, performance. This study indicates that several limitations obstruct widespread adoption AI/DL commercial applications. Recommendations overcoming these summarized.

Language: Английский

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Biodiversity indicators for result-based agri-environmental schemes – Current state and future prospects

Agricultural Systems, Journal Year: 2022, Volume and Issue: 204, P. 103538 - 103538

Published: Oct. 29, 2022

We systematically review proposed biodiversity indicators for result-based agri-environmental schemes.• Additionally, we synthesize currently used in Most studies and schemes focus on grasslands plant species diversity using vascular plants as indicators.• Policymakers can draw upon various options to design their Technological advances could improve the monitoring of indicators.

Language: Английский

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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

Energy and AI, Journal Year: 2022, Volume and Issue: 9, P. 100175 - 100175

Published: June 18, 2022

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities perform some difficult or dangerous tasks as well many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms its energy/power densities lifetime for service endurance. In this paper, current systems used UAVs comprehensively reviewed analyzed on existing configurations energy management systems. It identified that single type source not enough support achieve long-haul flight; hence, hybrid architecture necessary. To make use advantages each increase endurance good performance UAVs, containing two three types sources (fuel cell, battery, solar supercapacitor,) have be developed. regard, selection an appropriate structure with optimized efficient operation UAV. found data-driven models artificial intelligence (AI) promising intelligent management. This paper can provide insights guidelines future research development into design fabrication advanced

Language: Английский

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Cyber-agricultural systems for crop breeding and sustainable production

Trends in Plant Science, Journal Year: 2023, Volume and Issue: 29(2), P. 130 - 149

Published: Aug. 28, 2023

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) both breeding production agriculture. We discuss the progress perspective three fundamental components CAS - modeling, actuation emerging concept agricultural digital twins (DTs). also how CI is becoming a key enabler In this review we shed light on significance revolutionizing crop by enhancing efficiency, productivity, sustainability, resilience to changing climate. Finally, identify underexplored promising future directions for research development.

Language: Английский

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Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images

Environmental Pollution, Journal Year: 2023, Volume and Issue: 320, P. 120962 - 120962

Published: Jan. 5, 2023

Language: Английский

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Dos and Don'ts of using drone technology in the crop fields

Technology in Society, Journal Year: 2024, Volume and Issue: 76, P. 102456 - 102456

Published: Jan. 2, 2024

Language: Английский

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Review on the contribution of farming practices and technologies towards climate-smart agricultural outcomes in a European context

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 7, P. 100413 - 100413

Published: Feb. 15, 2024

The aim of this review was to provide an overview existing farming practices and technologies in Europe by assessing their contribution climate-smart agricultural (CSA) outcomes. Following the PRISMA protocol, 110 final selected studies were scrutinized. Altogether 74 different identified. Using inductive approach, identified categorized, potential towards contextualized CSA outcomes—productivity, resilience, GHG mitigation, biodiversity improvement, animal welfare support, water energy use efficiency—was assessed. Among practices, highlighted legume-based cover crops, crop rotation, intercropping, diversification as having promising achieve technologies, precision fertilization, protection, irrigation showed potential. Moreover, pasture grazing, feed additives, improved forage production holistic husbandry management with contributors emphasizes that utilization smart livestock systems could positively contribute achieving one or more Overall, mitigation farm productivity improvement outcomes relatively well covered reviewed literature. Improvements biodiversity, efficiency, are not demonstrated within studies.

Language: Английский

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UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs

The Plant Phenome Journal, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 19, 2024

Abstract We are in a race against time to combat climate change and increase food production by 70% feed the ever‐growing world population, which is expected double 2050. Agricultural research plays vital role improving crops livestock through breeding programs good agricultural practices, enabling sustainable agriculture systems. While advanced molecular technologies have been widely adopted, phenotyping as an essential aspect of has seen little development most African institutions remains traditional method. However, concept high‐throughput (HTP) gaining momentum, particularly context unmanned aerial vehicle (UAV)‐based phenotyping. Although into UAV‐based still limited, this paper aimed provide comprehensive overview understanding use UAV platforms image analytics for HTP identify key challenges opportunities area. The discusses field concepts, classification specifications, cases phenotyping, imaging systems processing methods. more required optimize UAVs’ performance data acquisition, limited studies focused on effect operational parameters acquisition.

Language: Английский

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Key Intelligent Pesticide Prescription Spraying Technologies for the Control of Pests, Diseases, and Weeds: A Review

Agriculture, Journal Year: 2025, Volume and Issue: 15(1), P. 81 - 81

Published: Jan. 1, 2025

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, make scientific decisions about pests, diseases, weeds; formulate personalized precision control plans; prevent pests through use of intelligent equipment. This study discusses IPSS from four perspectives: target information acquisition, processing, spraying, implementation control. acquisition section, identification based on images, remote sensing, acoustic waves, electronic nose are introduced. processing methods such as pre-processing, feature extraction, pest disease identification, bioinformatics analysis, time series data addressed. impact selection, dose calculation, time, method resulting effect formulation in a certain area explored. implement vehicle automatic technology, droplet characteristic technology their applications studied. addition, this future development prospectives IPPS technologies, including multifunctional systems, decision-support systems generative AI, sprayers. The advancement these will enhance agricultural productivity more efficient, environmentally sustainable manner.

Language: Английский

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A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

Sensors, Journal Year: 2021, Volume and Issue: 21(23), P. 7889 - 7889

Published: Nov. 26, 2021

The agriculture sector is one of the backbones many countries' economies. Its processes have been changing to enable technology adoption increase productivity, quality, and sustainable development. In this research, we present a scientific mapping precision techniques breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, used 4694 documents from Web Science database perform Bibliometric Performance Network Analysis literature using SciMAT software with support PICOC protocol. Our findings presented 22 strategic themes related Agriculture, such as Internet Things (IoT), Unmanned Aerial Vehicles (UAV) Climate-smart Agriculture (CSA), among others. thematic network structure nine most important clusters (motor themes) was an in-depth discussion performed. evolution map provides broad perspective how field has evolved over time 1994 2020. addition, our results discuss main challenges opportunities for research practice study. provide comprehensive overview These show subjects analyzed on topic basis insights future research.

Language: Английский

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