AIMC Topic: Crops, Agricultural

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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

Communications biology Apr 15, 2020
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segme...
Neural Networks, Computer Seeds Crops, Agricultural Hordeum Phenotype Deep Learning Image Processing, Computer-Assisted
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Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.

Sensors (Basel, Switzerland) Feb 29, 2020
Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and h...
Agriculture Crops, Agricultural Temperature Deep Learning
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An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.

Scientific reports Jan 8, 2020
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...
Forestry Brazil Bees Neural Networks, Computer Pollination Crops, Agricultural Animals Feeding Behavior Ecosystem Behavior, Animal
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Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.

The Plant journal : for cell and molecular biology Dec 9, 2019
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physio...
Machine Learning Phenotype Deep Learning Crops, Agricultural Hyperspectral Imaging Crop Production Genetic Association Studies Abelmoschus High-Throughput Screening Assays Salt Stress Salt-Tolerant Plants
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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology Jul 22, 2019
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...
Crops, Agricultural Breeding Environment Phenotype Wind Deep Learning Agriculture Triticum Motion Algorithms
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Optimization and control of the light environment for greenhouse crop production.

Scientific reports Jun 17, 2019
Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is...
Photosynthesis Crop Production Crops, Agricultural Cucumis Support Vector Machine Humans Models, Statistical Temperature Plant Leaves Seedlings Light Carbon Dioxide
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Distillation of crop models to learn plant physiology theories using machine learning.

PloS one May 29, 2019
Convolutional neural networks (CNNs) can not only classify images but can also generate key features, e.g., the Google neural network that learned to identify cats by simply watching YouTube videos, for the classification. In this paper, crop models ...
Oryza Algorithms Neural Networks, Computer Crops, Agricultural Deep Learning Machine Learning Models, Theoretical Plant Physiological Phenomena
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Recognition pest by image-based transfer learning.

Journal of the science of food and agriculture Apr 22, 2019
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, ...
Neural Networks, Computer Machine Learning Animals Image Processing, Computer-Assisted Plant Weeds Insecta Pest Control Humans Crops, Agricultural Plant Diseases Weed Control
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Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Heredity Mar 25, 2019
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...
Plant Breeding Chimera Quantitative Trait Loci Quantitative Trait, Heritable Genomics Oryza Models, Genetic Crops, Agricultural Seeds Support Vector Machine Crosses, Genetic Metabolomics Regression Analysis
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Robotic weeders can improve weed control options for specialty crops.

Pest management science Feb 28, 2019
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...
Crop Production Robotics Weed Control Crops, Agricultural Herbicides
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