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The bean book : 100 recipes for cooking with all kinds of beans, from the rancho gordo kitchen
\"From the founder of the acclaimed Rancho Gordo bean company, Steve Sando's authoritative presentation of 50 bean varieties and how to cook with them, from classic recipes to new takes\"-- Provided by publisher.
BOOK
Correction: Narrow Bottlenecks Affect Pea Seedborne Mosaic Virus Populations during Vertical Seed Transmission but not during Leaf Colonization
PLoS Pathog 10(1): e1003833 doi:10.1371/journal.ppat.1003833. * View Article * PubMed/NCBI * Google Scholar Citation: The PLOS Pathogens Staff (2014) Correction: Narrow Bottlenecks Affect Pea Seedborne Mosaic Virus Populations during Vertical Seed Transmission but not during Leaf Colonization.
Journal Article
Frank and Bean
\"When the introspective Frank meets the gregarious Bean, can they find a way to make beautiful music together?\"-- Provided by publisher.
Book
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
There are many types of haricot beans, the nutrient consumed all over the world. Each type differs in terms of features such as taste, size, economic value, etc. But even if they are different types, bean grains are frequently confused with each other. For these reasons, it is important to separate the bean grains of different species. For this purpose, a haricot bean dataset consisting of 33,064 images of 14 different bean types was created. By using these images, 3 different pre-trained Convolutional Neural Networks (CNN) were trained via the transfer learning method. Within the scope of the study, InceptionV3, VGG16, and VGG19 CNN models were used. These models were utilized for both end-to-end classification and extraction of image features. Firstly, the images were classified via Inception V3, VGG16, and VGG19 models. As a result of this classification, 84.48%, 80.63%, and 81.03% classification success were obtained from InceptionV3, VGG16, and VGG19 models, respectively. Secondly, the image features of these 3 models were taken from the layer just before the classification layer. Then, these features were given as input to the Support Vector Machine (SVM) and Logistic Regression (LR) models. Images were classified using six different models, InceptionV3 + SVM, VGG16 + SVM, VGG1 + SVM and InceptionV3 + LR, VGG16 + LR, VGG1 + LR. Classification successes obtained from InceptionV3 + SVM, VGG16 + SVM, and VGG19 + SVM were 79.60%, 81.97%, 80.64%, respectively. And, the classification successes obtained from InceptionV3 + LR, VGG16 + LR, and VGG19 + LR were 82.35%, 83.71%, and 83.54%, respectively. The InceptionV3, among all models, was determined to be the best classification model with a classification success of 84.48%. On the other hand, the model with the lowest classification success was determined to be the InceptionV3 + SVM. Detailed analysis of the created models was also carried out with precision, recall, and F-1 score metrics. It is thought that the proposed models can be used to distinguish haricot bean types in a quick and accurate way. Furthermore, the proposed computer vision methods can be combined with robotic systems and used to the distinction of bean types. By means of image processing, varieties can be determined on conveyor belts, and dry bean varieties can be purified with delta robots.
Journal Article
Quantifying country-to-global scale nitrogen fixation for grain legumes
Background
We collated estimates of the percentage of legume N derived from atmospheric N
2
(%Ndfa) for 14 major grain legumes and then analysed and aggregated the data to derive average values for different crops and regions/countries. The effects of cultivation year and whether data collected from experimental plots were relevant to crops growing in farmers’ fields were examined.
Scope
A total of 5374 %Ndfa estimates (observations) were sourced, 4205 from field experiments and 1169 on-farm measurements collected from farmer-grown crops. The largest number of reports (82) and %Ndfa estimates (1391) were for soybean.
Conclusions
The %Ndfa estimates for each legume species were consistent across years, except for soybean in North America. For some species estimates were also similar across geographic regions. There were no significant differences (
P
> 0.05) between estimates of %Ndfa derived from experimental plots and farmer-grown legume crops for nine of the 10 crops evaluated. Three distinct groups were identified with statistically-similar average %Ndfa values with associated standard deviations, namely: pigeonpea, faba bean and lupin – 74±11.8 %; groundnut, green and black gram, cowpea, chickpea, field pea, lentil, vetches and Bambara groundnut – 62±13.4 %; common bean – 38±11.1 %. There were three distinct different regional groupings for soybean: Brazil – 78±6.3 %; North America, Argentina, Asia, Africa and Oceania – 61±14.0 %; Europe – 44±13.8 %. Our findings provide more certainty and simplify the challenge of using field-scale measures of legume %Ndfa to estimate country-to-global inputs of fixed N from grain legumes.
Journal Article
Jack and the beanstalk and the french fries
In this humorous version of the traditional tale, Jack's magic beanstalk produces so many beans that soon everyone in the village is sick of eating them, and mad at Jack, and when he climbs the beanstalk he finds that Mr. Giant is equally fed up with beans--but fortunately Mrs. Giant suggests a solution to their diet problem.
Book
High-Moisture Meat Analogues Produced from Yellow Pea and Faba Bean Protein Isolates/Concentrate: Effect of Raw Material Composition and Extrusion Parameters on Texture Properties
Yellow pea and faba bean are potential candidates to replace soybean-based ingredients due to their suitability for cultivation in the northern hemisphere, non-genetically modified organisms cultivation practice and low risk of allergenicity. This study examined the functionality of local yellow pea and faba bean protein isolates/concentrate as meat analogue products. The most critical factors affecting the texture properties of meat analogue were also determined. Extrusion was used to produce high-moisture meat analogues (HMMAs) from yellow pea and faba bean protein isolates/concentrates and HMMAs with fibrous layered structures was successfully produced from both imported commercial and local sources. The texture properties of the HMMA produced were mainly affected by the ash, fiber and protein content and water-holding capacity of the source protein. Three extrusion process parameters (target moisture content, extrusion temperature, screw speed), also significantly affected HMMA texture. In conclusion, functional HMMA can be produced using protein isolates derived from locally grown pulses.
Journal Article
How Martha saved her parents from green beans
A young girl must face her least favorite food when a mean gang of green beans kidnaps her parents.
Book
Nutritional, functional, and bioactive properties of african underutilized legumes
Globally, legumes are vital constituents of diet and perform critical roles in maintaining well-being owing to the dense nutritional contents and functional properties of their seeds. While much emphasis has been placed on the major grain legumes over the years, the neglected and underutilized legumes (NULs) are gaining significant recognition as probable crops to alleviate malnutrition and give a boost to food security in Africa. Consumption of these underutilized legumes has been associated with several health-promoting benefits and can be utilized as functional foods due to their rich dietary fibers, vitamins, polyunsaturated fatty acids (PUFAs), proteins/essential amino acids, micro-nutrients, and bioactive compounds. Despite the plethora of nutritional benefits, the underutilized legumes have not received much research attention compared to common mainstream grain legumes, thus hindering their adoption and utilization. Consequently, research efforts geared toward improvement, utilization, and incorporation into mainstream agriculture in Africa are more convincing than ever. This work reviews some selected NULs of Africa (Adzuki beans ( Vigna angularis ), African yam bean ( Sphenostylis stenocarpa ), Bambara groundnut ( Vigna subterranea ), Jack bean ( Canavalia ensiformis ), Kidney bean ( Phaseolus vulgaris ), Lima bean ( Phaseolus lunatus ), Marama bean ( Tylosema esculentum ), Mung bean, ( Vigna radiata ), Rice bean ( Vigna Umbellata ), and Winged bean ( Psophocarpus tetragonolobus )), and their nutritional, and functional properties. Furthermore, we highlight the prospects and current challenges associated with the utilization of the NULs and discusses the strategies to facilitate their exploitation as not only sources of vital nutrients, but also their integration for the development of cheap and accessible functional foods.
Journal Article