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Crops, Agricultural

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Recognition pest by image-based transfer learning.

Journal of the science of food and agriculture
BACKGROUND: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, ...

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Heredity
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available fo...

Robotic weeders can improve weed control options for specialty crops.

Pest management science
Specialty crop herbicides are not a priority for the agrochemical industry, and many of these crops do not have access to effective herbicides. High-value fruit and vegetable crops represent small markets and high potential liability in the case of h...

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

GigaScience
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent a...

A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.

Environmental science and pollution research international
Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA)...

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors.

Sensors (Basel, Switzerland)
Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex informa...

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

PLoS computational biology
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. ...

Robotic bees for crop pollination: Why drones cannot replace biodiversity.

The Science of the total environment
The notion that robotic crop pollination will solve the decline in pollinators has gained wide popularity recently (Fig. 1), and in March 2018 Walmart filed a patent for autonomous robot bees. However, w present six arguments showing that this is a t...

Vineyard water status assessment using on-the-go thermal imaging and machine learning.

PloS one
The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estima...