Pest Detection for Precision Agriculture Based on IoT Machine Learning

2020 ◽  
pp. 65-72
Author(s):  
Andrea Albanese ◽  
Donato d’Acunto ◽  
Davide Brunelli
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Pest Detection

scholarly journals Energy Neutral Machine Learning Based IoT Device for Pest Detection in Precision Agriculture

2019 ◽  
Vol 2 (4) ◽  
pp. 10-13 ◽  
Author(s):  
Davide Brunelli ◽  
Andrea Albanese ◽  
Donato d'Acunto ◽  
Matteo Nardello
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Pest Detection

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

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.

UAV technology and machine learning techniques applied to the yield improvement in precision agriculture

2017 ◽  
Author(s):  
Jaen Alberto Arroyo ◽  
Cecilia Gomez-Castaneda ◽  
Elias Ruiz ◽  
Enrique Munoz de Cote ◽  
Francisco Gavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Yield Improvement ◽  
Learning Techniques

Analysis of Precision Agriculture Technique by Using Machine Learning and IoT

2020 ◽  
pp. 859-867
Author(s):  
Y. Sasi Supritha Devi ◽  
T. Kesava Durga Prasad ◽  
Krishna Saladi ◽  
Durgesh Nandan
Keyword(s):  
Machine Learning ◽  
Precision Agriculture

scholarly journals Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

2020 ◽  
Vol 167 (3) ◽  
pp. 037522 ◽  
Author(s):  
Yemeserach Mekonnen ◽  
Srikanth Namuduri ◽  
Lamar Burton ◽  
Arif Sarwat ◽  
Shekhar Bhansali
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

scholarly journals Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning

2021 ◽  
Author(s):  
Satvik Garg ◽  
Pradyumn Pundit ◽  
Himanshu Jindal ◽  
Hemraj Saini ◽  
Somya Garg
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Multimodal System
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