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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis

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Herbaceous field crops include several hundred plant species worldly widespread for different end-uses, from food to no-food applications. Among them are included cereals, grain legumes, sugar beet, potato, cotton, tobacco, sunflower, safflower, rape, flax, soybean, alfalfa, clover spp. and other fodder crops, but only 15–20 species play a relevant role for the worldly global economy. Nowadays, to meet the food demand of the ever-increasing world population in a scenario of decreased arable lands, the development of holistic agricultural management approaches to boost contemporaneously yield and quality of herbaceous field crops is essential. Accordingly, this book represents an up-to-date collection of the current understanding of the impact of several agricultural management factors (i.e., genetic selection, planting density and arrangement, fertilization, irrigation, weed control and harvest time) on the yield and qualitative performances of 11 field crops (wheat, cardoon, potato, clary sage, basil, sugarcane, canola, cotton, tomato, lettuce and hemp). On the whole, the topics covered in this book will ensure students and academic readers, such as plant physiologists, environmental scientists, biotechnologists, botanists, soil chemists and agronomists, to get the information about the recent research advances on the eco-sustainable management cultivation of herbaceous field crops, with a particular focus on varietal development, soil nutrient and water management, weed control, etc.

planting density --- fertilization --- the central composite design --- fiber yield --- analog optimization --- potato --- nitrogen fertilization --- environmental sustainability --- cost-effective --- nitrogen use efficiency --- tuber yield --- EONFR --- growth --- specific leaf nitrogen --- critical nitrogen uptake --- cotton --- dry matter yield --- root growth --- root physiology --- water productivity --- nitrogen productivity --- drip irrigation quota --- lint yield --- biomass --- leaf chlorophyll fluorescence --- leaf gas exchange --- leaf structure --- drought tolerance --- dry weight yield --- essential oil content --- leaf area index --- Ocimum basilicum --- potassium --- fertilizer --- biomass accumulation --- fiber quality --- organic farming system --- yield --- pH --- soluble solid content --- Bostwick viscosity --- phosphorus sensitivity --- phosphorus --- reproductive organ biomass --- nutrients accumulation --- plant density --- nitrogen fertilization rate --- photosynthesis rate --- SPAD readings --- nitrogen efficiency indices --- tuber nutritional composition --- cereal crops --- plant water extracts --- bioherbicides --- weed management --- allelopathy --- dual purpose canola --- nitrogen fertilizer --- oil content --- grazing --- sustainable agriculture --- integrated weed management --- yield losses --- preventive weed control --- mechanical weed control --- physical weed control --- biological weed control --- herbicides --- hybrids --- wheat --- weeds --- competition --- genetic gain --- genomic selection --- quantitative genetics --- sugarcane breeding --- pit plantation --- planting patterns --- ratoon crop --- sowing techniques --- sugarcane yield --- quality --- seasonal variation --- fatty acids --- free sugars --- chemical composition --- Cynara cardunculus L. --- cardoon --- organic acids --- clary sage --- essential oil --- aromatic plant species --- biometric and agronomic characteristics --- arbuscular mycorrhizal fungi --- organic farming --- calcareous soils --- crop physiology --- sustainability --- diatomaceous earth --- monosilicic acid --- Si application method --- soil water conditions --- wheat cultivar --- tocopherols --- lipidic fraction --- companion plants --- N-fertilization --- partial land equivalent ratio (PLER) --- weed control --- grain quality --- productivity --- n/a

Choose an application

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

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis