Reprint

Advanced Technologies in Agricultural Engineering and Energy Optimization

Edited by
May 2024
174 pages
  • ISBN978-3-7258-0920-2 (Hardback)
  • ISBN978-3-7258-0919-6 (PDF)

This is a Reprint of the Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

The use of renewable energy plays an important role in agriculture, where technologies are also being improved from year to year; agricultural production is growing, and machinery and systems are becoming more autonomous and robotic, which is no longer possible without complex computing, optimization, planning, and working with large amounts of data. Nowadays, a large amount of unstructured heterogeneous data fuels the demand to extract useful insights in an automatic, reliable, and scalable way. The agriculture sector, however, is historically less receptive to innovation and lags behind the implementation of contemporary solutions, which defines the relevance of this Special Issue. This reprint covers high-quality papers from academics and industry-related researchers in the areas of power supply in rural areas, biofuels and renewable energies used in agriculture, energy efficiency and conservation in agriculture, agricultural robotic applications, livestock production, application of electrophysical impact on agricultural objects, technologies in harvesting and seed machinery, solutions for digital and precision agriculture, applied mathematics, environmental bioengineering, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to show the most recently advanced methods.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
object tracking; convolution neural network; AI; siamese network; image similarity; CUDA; Python; PyTorch; computer vision; wind energy conversion system; doubly-fed induction generator; MPPT; vector control; renewable energy; WECS; DFIG; greenhouse gases (GHG); rape straw pellets; biodiesel; combustion; CO2; CH4; NOx emission levels; pellet boiler; tractor engine; red leaf mustard; light-emitting diode; spectral composition of light; productivity; photosynthesis; district heating; urban waste heat; demonstration sites; business aspects; particulate matter (PM); rapeseed straw pellets; biodiesel; combustion; PM emission levels; pellet boiler; diesel engine; engine dynamometer; received signal strength indicator (RSSI); trilateration; indoor localization; kalman filter (KF); support vector regression (SVR); generalized regression neural network (GRNN); sensorless control; extended Kalman filter; fractional order control; fractional calculus; non-integer integral-differential equations; power transformer; oil-immersed insulation; moisture forecasting; long short-term memory; CNN; deep learning; ensemble learning; Inception–ResNet; EfficienNet; DenseNet; MSW; image classification; computational cost

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