An IoT and Machine Learning-Based Crop Prediction System for Precision Agriculture

A machine learning approach for prediction system and analysis of nutrients uptake for better crop growth in the Hydroponics system

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) ◽

10.1109/icais50930.2021.9395956 ◽

2021 ◽

Author(s):

Ms Swapnil Verma ◽

Sushopti D. Gawade

Keyword(s):

Machine Learning ◽

Crop Growth ◽

Learning Approach ◽

Prediction System ◽

Nutrients Uptake ◽

Machine Learning Approach

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

Scientific Reports ◽

10.1038/s41598-021-81005-0 ◽

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.

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Soil Sensors Based Prediction System for Plant Diseases using Exploratory Data Analysis and Machine Learning

IEEE Sensors Journal ◽

10.1109/jsen.2020.3046295 ◽

2020 ◽

pp. 1-1

Author(s):

Manish Kumar ◽

Ahlad Kumar ◽

Vinay S. Palaparthy

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Plant Diseases ◽

Prediction System ◽

Soil Sensors ◽

Exploratory Data

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A Real-Time Bus Arrival Time Prediction System Based on Spark Framework and Machine Learning Approaches: a case study in Chiang Mai

2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering ◽

10.1109/ectidamtncon51128.2021.9425771 ◽

2021 ◽

Author(s):

Li Ye ◽

Pree Thiengburanathum ◽

Poon Thiengburanathum

Keyword(s):

Machine Learning ◽

Arrival Time ◽

Learning Approaches ◽

Prediction System ◽

Chiang Mai ◽

Time Prediction ◽

Arrival Time Prediction ◽

Bus Arrival Time Prediction ◽

Spark Framework

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Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India

Medical Journal Armed Forces India ◽

10.1016/j.mjafi.2020.10.013 ◽

2021 ◽

Author(s):

Ekta Maini ◽

Bondu Venkateswarlu ◽

Baljeet Maini ◽

Dheeraj Marwaha

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Exploratory Study ◽

South India ◽

Indian Population ◽

Disease Prediction ◽

Prediction System

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Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Remote Sensing ◽

10.3390/rs13030531 ◽

2021 ◽

Vol 13 (3) ◽

pp. 531

Author(s):

Caiwang Zheng ◽

Amr Abd-Elrahman ◽

Vance Whitaker

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Precision Agriculture ◽

Image Data ◽

Fruit Shape ◽

Biological Information ◽

Genetic Traits ◽

Multiple Sensors ◽

Fruit Maturity ◽

High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

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