scholarly journals Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Karim Ennouri ◽  
Slim Smaoui ◽  
Yaakoub Gharbi ◽  
Manel Cheffi ◽  
Olfa Ben Braiek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Crop Production ◽  
Significant Contribution ◽  
New Technology ◽  
Emerging Technology ◽  
Agricultural System ◽  
Task Load ◽  
Agriculture Sector ◽  
Central Factors

Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.

scholarly journals Nanotechnology: An Elixir to Life Sciences

2019 ◽  
Author(s):  
Zulqurnain Khan ◽  
Sultan Habibullah Khan ◽  
Muhammad Zubair Ghouri ◽  
Hina Shahzadi ◽  
Sarmad Frogh Arshad ◽  
...  
Keyword(s):  
Crop Production ◽  
Food Packaging ◽  
Insect Pests ◽  
New Technology ◽  
Food Storage ◽  
Technological Advancement ◽  
Food Sector ◽  
New Era ◽  
Agriculture Sector ◽  
Food And Agriculture

Agriculture sector is the backbone of developing countries for their economy. Growing world’s population is putting more pressure on agriculture sector and there is a need to develop new technology to address the crises of food safety and food shortage. Today’s agriculture has been entered in a new era where nanotechnology works as a technological advancement regarding entire agriculture crops, and food sector revolution, even though, it has prodigious applications in food production, food processing, food packaging, food storage and economic growth of industries. Moreover, nanotechnology is the best solution to solve problems related to better food and agriculture. Likewise, biotechnology, nanotechnology also raised high concerns upon safety on biodiversity, health and environment. Nanotechnology is providing efficient alternatives to increase the crop production by managing the insect/pests in agriculture in an eco-friendly manner. It also promotes plant efficiency to absorb nutrients. However, the concerns are very high regarding regulation, safety and approval of nanotechnology products by risk assessment authorities. The suggested review includes stupendous applications of nanotechnology in food and agriculture sector along with its prospective merits and associated risks.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

scholarly journals Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”

2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Deep Learning ◽  
Special Issue ◽  
The Earth

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]

scholarly journals Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pierre Auloge ◽  
Julien Garnon ◽  
Joey Marie Robinson ◽  
Sarah Dbouk ◽  
Jean Sibilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Interventional Radiology ◽  
Clinical Practice ◽  
Medical Students ◽  
Medical Curriculum ◽  
New Technology ◽  
Large Population ◽  
The Other ◽  
Clinical Phase ◽  
Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

scholarly journals Potential applications of biogenic selenium nanoparticles in alleviating biotic and abiotic stresses in plants: A comprehensive insight on the mechanistic approach and future perspectives

2021 ◽  
Vol 10 (1) ◽  
pp. 456-475
Author(s):  
Efat Zohra ◽  
Muhammad Ikram ◽  
Ahmad A. Omar ◽  
Mujahid Hussain ◽  
Seema Hassan Satti ◽  
...  
Keyword(s):  
Abiotic Stresses ◽  
Crop Production ◽  
Plant Secondary Metabolites ◽  
Selenium Nanoparticles ◽  
Future Research ◽  
Biotic And Abiotic Stresses ◽  
Biotic Stresses ◽  
Agriculture Sector ◽  
Developmental Growth ◽  
Potential Applications

Abstract In the present era, due to the increasing incidence of environmental stresses worldwide, the developmental growth and production of agriculture crops may be restrained. Selenium nanoparticles (SeNPs) have precedence over other nanoparticles because of the significant role of selenium in activating the defense system of plants. In addition to beneficial microorganisms, the use of biogenic SeNPs is known as an environmentally friendly and ecologically biocompatible approach to enhance crop production by alleviating biotic and abiotic stresses. This review provides the latest development in the green synthesis of SeNPs by using the results of plant secondary metabolites in the biogenesis of nanoparticles of different shapes and sizes with unique morphologies. Unfortunately, green synthesized SeNPs failed to achieve significant attention in the agriculture sector. However, research studies were performed to explore the application potential of plant-based SeNPs in alleviating drought, salinity, heavy metal, heat stresses, and bacterial and fungal diseases in plants. This review also explains the mechanistic actions that the biogenic SeNPs acquire to alleviate biotic and abiotic stresses in plants. In this review article, the future research that needs to use plant-mediated SeNPs under the conditions of abiotic and biotic stresses are also highlighted.

scholarly journals A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jane Scheetz ◽  
Philip Rothschild ◽  
Myra McGuinness ◽  
Xavier Hadoux ◽  
H. Peter Soyer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
New Zealand ◽  
Radiation Oncology ◽  
Online Survey ◽  
New Technology ◽  
Medical Liability ◽  
Healthcare Outcomes ◽  
Medical Workforce ◽  
Artificial Intelligence Technology ◽  
Workforce Needs

AbstractArtificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

scholarly journals Kinetics of slow release of nitrogen fertiliser from multi-layered nanofibrous structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leila Javazmi ◽  
Anthony Young ◽  
Gavin J. Ash ◽  
Tobias Low
Keyword(s):  
Crop Production ◽  
Slow Release ◽  
New Technology ◽  
Single Layer ◽  
Middle Layer ◽  
Modern Agriculture ◽  
Triple Layer ◽  
Urea Release ◽  
Electrospun Nanofibres ◽  
Kinetics Of

AbstractFertilisers are essential in modern agriculture to enhance plant growth, crop production and product quality. Recent research has focused on the development of delivery systems designed to prolong fertiliser release. This study introduces a new technology to encapsulate and release molecules of fertilisers by using multi-layered electrospun nanofibre as a carrier. Single-layer poly L-lactic acid (PLLA) nanofibres loaded with urea were fabricated using electrospinning. Triple-layer nanofibrous structures were produced by electrospinning polyhydroxybutyrate (PHB) nanofibres as external layers with PLLA nanofibres impregnated with urea fertiliser as the middle layer. Scanning electron microscopy (SEM) and Fourier transform infrared spectrophotometry (FTIR) were employed to characterize the morphology of electrospun nanofibres. Urea release dynamic was analysed using a total nitrogen instrument (TNM-1). The results indicated that triple-layered urea-impregnated nanofibrous structures led to lower initial rate of nitrogen release and slower release rate of cumulative nitrogen which extended for more than three months. It is concluded that triple-layer nanofibrous structures have the potential for slow release delivery of fertilisers.

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