AIMC Topic: Agriculture

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Machine learning in agriculture: from silos to marketplaces.

Plant biotechnology journal Dec 20, 2020
Machine Learning Agriculture
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An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.

PloS one Dec 17, 2020
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such a...
Image Processing, Computer-Assisted Algorithms Plants Humans Agriculture Deep Learning Canada Plant Development Machine Learning
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Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords.

Traffic injury prevention Nov 18, 2020
OBJECTIVE: Traditionally, structured or coded data fields from a crash report are the basis for identifying crashes involving different types of vehicles, such as farm equipment. However, using only the structured data can lead to misclassification o...
Algorithms Forecasting Agriculture Humans Machine Learning Louisiana Support Vector Machine Bayes Theorem Accidents, Traffic Texas
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Whole-Field Reinforcement Learning: A Fully Autonomous Aerial Scouting Method for Precision Agriculture.

Sensors (Basel, Switzerland) Nov 18, 2020
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, fligh...
Zea mays Aircraft Machine Learning Agriculture Neural Networks, Computer
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Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.

Sensors (Basel, Switzerland) Nov 11, 2020
Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimiz...
Solanum lycopersicum Coronavirus Infections SARS-CoV-2 Agriculture Pneumonia, Viral Humans Climate Pandemics Betacoronavirus COVID-19 Artificial Intelligence
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The parable of arable land: Characterizing large scale land acquisitions through network analysis.

PloS one Oct 13, 2020
Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasi...
Agriculture Internationality Neural Networks, Computer Databases, Factual Natural Resources
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The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems.

Current opinion in biotechnology Oct 7, 2020
Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current an...
Agriculture Remote Sensing Technology Food Supply Climate Change Humans Artificial Intelligence
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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

PloS one Sep 28, 2020
This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's To...
Satellite Imagery Deep Learning Agriculture Vegetables Nicotiana Triticum Humans Pakistan Data Accuracy
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Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.

Sensors (Basel, Switzerland) Sep 16, 2020
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the...
Machine Learning Remote Sensing Technology Least-Squares Analysis Agriculture Aircraft Plant Tubers Solanum tuberosum Seasons Spectrum Analysis
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Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Sensors (Basel, Switzerland) Sep 9, 2020
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. ...
Zea mays Chlorophyll Machine Learning Plant Leaves Remote Sensing Technology Agriculture
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