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This book deals with sustainable agriculture at a time of climate change. It seeks to identify a number of solutions to deal with the agricultural stresses caused by climate change. These range from the identification and cultivation of appropriate crop varieties and the adoption of climate adaptive agricultural practices. Significant sustainable agricultural innovation is required to deal with these challenges. Intellectual property rights (IPRs) may be of crucial importance for modern agriculture. They serve to make R&D in agriculture attractive, by encouraging investment in new technologies and generating tradeable assets. A number of the chapters of this book refer to the principal IPRs relevant to agricultural innovation, namely: (i) patents, which protect inventions; (ii) plant variety rights, which protect the breeding of new and distinct plant varieties; and (iii) trademarks and geographical indications, which facilitate the marketing of products by providing protection for the symbols of their manufacturing or geographic origin. The United Nations Climate Change Panel has urged the consideration of the agricultural practices of traditional communities and some of these practices particularly involving rice, banana, and brassica cultivation are explored in the book. This book is essential reading for officials of governments and international organizations concerned with sustainability, as well as scholars and students concerned with these subjects

Research. --- Biology. --- Technology. --- Engineering. --- Agriculture. --- alternative energy source --- Ethiopian mustard --- sustainability --- agricultural green development --- entropy weight method --- spatial heterogeneity --- spatial spillover effect --- China --- rice yields --- climate change --- phenology --- relative contribution --- partial correlation --- seed security --- banana tissue culture planting material --- uptake --- banana farmers --- central Uganda --- traditional rice economics --- institutional --- socio-demographic factors --- multinomial logit model --- constraints --- China-Africa cooperation --- agricultural program --- agricultural training --- technology adoption --- dams --- agriculture --- livelihoods --- health --- schistosomiasis --- restoration --- sustainable development --- climate adaptation --- rice–wheat cropping system --- South Asia --- water requirements --- nitrogen --- direct seeding --- agricultural science, technology and innovation --- Innovation efficiency --- DEA --- G20 --- productivity --- efficiency --- food security --- digital agriculture --- smart farming --- digitalization --- digital technologies --- Middle East and North Africa --- decision making --- instrumental variable --- neighbourhood effects --- rice farmers --- risk attitudes --- spatial dependence --- agricultural innovation --- sustainable agriculture --- plant breeding --- cereals --- intellectual property --- agricultural law --- plant variety rights --- seed marketing --- European Union --- fertilizer --- knowledge --- attitude --- ease of use --- motivation --- work performance --- n/a --- rice-wheat cropping system

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill–Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching–learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- n/a --- Hill-Tsai failure criterion --- teaching-learning