Engineering of Smart Agriculture

doi:10.3390/books978-3-0365-9497-2

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Engineering of Smart Agriculture

Edited by

November 2023

508 pages

ISBN978-3-0365-9496-5 (Hardback)
ISBN978-3-0365-9497-2 (PDF)

https://doi.org/10.3390/books978-3-0365-9497-2

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This book is a reprint of the Special Issue Engineering of Smart Agriculture that was published in

Biology & Life Sciences

Chemistry & Materials Science

Computer Science & Mathematics

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

This Special Issue presents an extract from the reality of smart agriculture, where the combination of modern technologies, innovative solutions, and sustainable approaches to food production classifies this part of science as highly interdisciplinary, multifaceted, and technologically advanced. The need to increase productivity, optimize natural resources, and minimize environmental impact requires new approaches. In this context, smart agriculture is emerging as a solution that combines technology, data, and science to achieve sustainable, efficient, and innovative food production. This Special Issue introduces the field of smart farming, which encompasses a range of advanced technologies. The use of these tools allows for the monitoring and optimization of crop conditions, precise fertilization, the minimization of water and energy usage, and the improvement of crop quality and quantity. In addition, plant monitoring systems are described, which, by means of sensors and data analysis, provide farmers with valuable information about plant health, soil moisture, temperature, and other factors affecting crop growth. A significant part of this monograph deals with the automation of agricultural processes, where robots and machines undertake tasks with high precision and accuracy, contributing to the farmer’s efficiency. This Special Issue aims not only to provide an understanding of smart agriculture but to also inspire the reader to think about the future of agriculture and the ways in which modern food production methods can be improved.

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Hardback

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Keywords

recirculating aquaculture system; variable-flow regulation model; circulating pump-drum filter linkage working technique; machine learning methods; gene algorithm support vector machine; controlled environment agriculture; digital twin; productivity; architecture; optimization; NDVI; image processing; SURF; SIFT; SVM; BP algorithm; performance; sweet pepper; deep neural network; sprouts; stimulation with a pulsed magnetic field; micro and macro components; ICP-OES; ground pressure; paddy soil; seeding skateboard; internet of things; wireless measurement system; calcium; magnesium; phosphorus; potassium; copper; iron; manganese; sodium; zinc; wild leafy vegetables; real-time kinematic (RTK); precision agriculture; ISO standard; global positioning system (GPS); GLONASS; agricultural tractor; Korean ginseng; root-rot-disease; plant segmentation; deep learning; Spodoptera frugiperda; deep learning; convolutional neural network; corn insect; electrical fields; magnetic fields; high-voltage electric field; growth of fruits; ripening of fruits; shelf life of fruits; fungicide resistance; PCR; broad-spectrum fungicides; Fusarium solani; toxicity; asymmetric machine-tractor unit; motion; stability; resistance coefficients; amplitude-frequency characteristic; phase-frequency characteristic; magnetic fields; quality parameters; firmness; total soluble solid; titratable acidity; dry matter; respiration rate; apple fruit; model predictive control; energy management system; renewable energy; smart irrigation; agriculture 4.0; maize bulk; kernel breakage; vertical pressure; deformation; heat production; sustainable; agriculture; rural; mobile internet technology; bivariate probit model; agricultural modernization; Pakistan; wheat; smart; Zea mays; stalk diseases; crop rotation; stubble; suppressive soils; biomass combustion; broadleaved tree; pulsed electric field; calorific value; UV-C radiation; stress response; mechanical properties; stimulation; potato tuber; CFD (Computational Fluid Dynamics); terminal velocity; winnowing machine separation; chili pepper harvester; water and fertilizer integration; pH adjustment; BP-PID-Smith algorithm; estimated compensation; deep learning; convolutional neural networks; MATLAB; hybrid system; mobile application; productivity; aeration; airflow resistance; pore volume; semi-empirical modelling; self-compaction; spatial and temporal; wheat; energy; environmental contamination; lettuce; life cycle assessment; winter wheat; leaf greenness index (SPAD); protein; photosynthesis; nitrogen; phosphorus; smart agriculture; weed management; crop productivity; computer vision; deep learning; analytical procedure; forklift truck; interchangeable equipment; static stability assessment; typical meteorological sequence; typical meteorological week; wastewater treatment; high-rate algae pond; solar irradiance; Finkelstein-Schafer statistics; southern rice; threshing loss; fuzzy PID; adaptive algorithm; hilly mountainous areas; rapeseed pod; surface area measurement; image processing; 3-D measurement; side area of oblique cylinders; rapeseed pod seed testing machine; n/a