scholarly journals Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 171
Author(s):  
Thomas Fahey ◽  
Hai Pham ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Dario Stefanelli ◽  
...  
Keyword(s):  
Crop Yield ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Multispectral Imaging ◽  
Plant Stress ◽  
Plant Diseases ◽  
Added Value ◽  
High Rate ◽  
Future Research ◽  
Atmospheric Conditions

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

scholarly journals Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook

2020 ◽  
Vol 12 (9) ◽  
pp. 1491 ◽  
Author(s):  
Gaetano Messina ◽  
Giuseppe Modica
Keyword(s):  
Precision Agriculture ◽  
State Of The Art ◽  
Plant Stress ◽  
Stress Conditions ◽  
Plant Diseases ◽  
Basic Knowledge ◽  
Plant Phenotyping ◽  
Future Research ◽  
Thermal Data ◽  
Low Altitude

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.

scholarly journals Advances in automatic identification of flying insects using optical sensors and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carsten Kirkeby ◽  
Klas Rydhmer ◽  
Samantha M. Cook ◽  
Alfred Strand ◽  
Martin T. Torrance ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Insect Pests ◽  
Crop Damage ◽  
Automatic Identification ◽  
Flying Insects ◽  
Remote Sensor ◽  
Promising Solution ◽  
Environmentally Sensitive

AbstractWorldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

scholarly journals The Bioactive Potential of Trawl Discard: Case Study from a Crinoid Bed Off Blanes (North-Western Mediterranean)

Marine Drugs ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 83
Author(s):  
Alfredo García-de-Vinuesa ◽  
Montserrat Demestre ◽  
Arnau Carreño ◽  
Josep Lloret
Keyword(s):  
Bioactive Compounds ◽  
Fish Habitat ◽  
Antibacterial Properties ◽  
Marine Species ◽  
Western Mediterranean ◽  
Bioactive Molecules ◽  
Added Value ◽  
Future Research ◽  
Trawl Survey ◽  
Bioactive Potential

Although knowledge of the bioactive compounds produced by species inhabiting coastal waters is increasing, little is known about the bioactive potential produced by marine species occupying deeper habitats with high biodiversity and productivity. Here, we investigate about the bioactive potential of molecules produced by species that inhabit the crinoid beds, a poorly known essential fish habitat affected by trawling, wherein large amounts of commercial and noncommercial species are discarded. Based on a trawl survey conducted in 2019, 14% of the 64 species discarded on crinoid beds produce molecules with some type of bioactive potential, including; soft corals (Alcyonium palmatum); tunicates (Ascidia mentula); bony fish, such as horse mackerel (Trachurus trachurus); European hake (Merluccius merluccius); and chondrichthyans, such as small-spotted catshark (Scyliorhinus canicula). In addition, 16% of the discarded species had congeneric species that produce compounds with bioactive potential, indicating that such species might also possess similar types of bioactive molecules. Molecules with antioxidant, antitumour, antihypertensive, and antibacterial properties were the most frequent, which could provide the basis for future research aiming to discover new marine-based drugs and compounds for other human uses. Among all species or genera that produce compounds with bioactive potential, 68% presented medium or high vulnerability to trawling. Results show that the discarded catch contains many species, which produce different bioactive compounds that represent an added-value resource. These results highlight the importance of manage properly crinoid beds, to ensure that species that produce molecules with bioactive potential inhabiting these habitats are protected.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

scholarly journals Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 646
Author(s):  
Bini Darwin ◽  
Pamela Dharmaraj ◽  
Shajin Prince ◽  
Daniela Elena Popescu ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Soil Parameters ◽  
Learning Models ◽  
Weed Detection ◽  
Agricultural Industry ◽  
Agricultural Robots ◽  
Detection Techniques ◽  
Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

A Descriptive Analysis of Music Therapy Employment from 2013 to 2019

2020 ◽  
Author(s):  
Jonathon Oden
Keyword(s):  
Music Therapy ◽  
Descriptive Analysis ◽  
American Music ◽  
High Rate ◽  
Future Research ◽  
Full Time ◽  
Music Therapists ◽  
Funding Sources ◽  
Part Time ◽  
Therapy Services

Abstract The purpose of this study was to descriptively analyze music therapy employment data from 2013 to 2019, including years in the field, gender, age, ethnicity, hours worked, jobs created, number of new board-certified music therapists (MT-BCs), funding sources, and wages. A database was created to analyze descriptive data from the 2013–2019 American Music Therapy Association Workforce Analysis Surveys as well as data from the Certification Board for Music Therapists. Results indicate a large portion of music therapists (MTs) have been in the field for five years or less. Though the majority of MTs work full time, there is a high rate of part-time employment. An estimate of the total number of new full-time jobs represented a ratio of 57% of new MT-BCs during the period. Private pay was the most reported funding source for music therapy services. Music therapy wages tended to be higher for those with higher levels of education. Limitations of the study and suggestions for future research are provided.

scholarly journals Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology

2001 ◽  
Vol 1 ◽  
pp. 767-776 ◽  
Author(s):  
E.D. Lund ◽  
M.C. Wolcott ◽  
G.P. Hanson
Keyword(s):  
Electrical Conductivity ◽  
Case Studies ◽  
Crop Yield ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Management Practices ◽  
N Availability ◽  
Soil Electrical Conductivity ◽  
Variable Yield ◽  
Wide Range

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

scholarly journals Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6427
Author(s):  
Haoyu Niu ◽  
Derek Hollenbeck ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Large Scale ◽  
Bowen Ratio ◽  
Future Research ◽  
Accurate Estimation ◽  
Research Areas ◽  
Energy Balance Approach

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

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