Topic Review
Black Soldier Fly
The black soldier fly (BSF), Hermetia illucens Linnaeus, is a large Stratiomyidae fly (13-20 mm size) found worldwide, but it is believed to have originated in the Americas. It is frequently found in the tropics and temperate regions throughout the world. Although adapted primarily to these regions, it can tolerate wide extremes of temperature except when ovipositing. They are generally considered a beneficial insect and non-pest. The adult fly does not have mouthparts, stingers, or digestive organs; thus, they do not bite or sting and do not feed during its short lifespan. They feed only as larvae and are, therefore, not associated with disease transmission. BSF larvae (BSFL) are voracious eaters of a wide range of organic wastes, decomposing and returning nutrients to the soil. Additionally, BSFL is an alternative  protein source for aquaculture, pet food, livestock feed, and human nutrition.
  • 5689
  • 28 Feb 2021
Topic Review
Seed Morphology
Seed morphology is the scientific analysis and description of the shape of seeds.
  • 3321
  • 28 Oct 2020
Topic Review
Land Suitability Assessment
Land suitability assessment is a method of land evaluation, which identifies the major limiting factors for planting a particular crop. Land suitability assessment includes qualitative and quantitative evaluation. In the qualitative land suitability evaluations, information about climate, hydrology, topography, vegetation, and soil properties is considered and in quantitative assessment, the results are more detailed and yield is estimated. At present study we prepared land suitability assessment map for rain-fed wheat and barley crops based on FAO "land suitability assessment framework" using parametric method and machine learning algorithms in Kurdistan Province, located in west of Iran. This is a unique study that compared two machine learning-based and traditional-based approaches for mapping current and potential future land suitability classes. Moreover, potential yield of rain-fed wheat and barley crop were computed by FAO model.
  • 2021
  • 30 Oct 2020
Topic Review
Coffee By-Products
The coffee plant Coffea spp. offers much more than the well-known drink made from the roasted coffee bean. During its cultivation and production, a wide variety of by-products are accrued, most of which are currently unused, thermally recycled, or used as animal feed.
  • 1659
  • 02 Nov 2020
Topic Review
Advanced Agriculture Technology
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced in generating database by various information and advanced communication technologies, such as the Internet of Things (IoT). 
  • 1546
  • 25 May 2021
Topic Review
Wild Vigna Legumes
Legumes (family Fabaceae) represent the third largest family among flowering plants, consisting of approximately 650 genera and 20, 000 species which possess an undeniable vital nutritional value for both humans and animals due to their protein content. The genus Vigna is a huge and important set of legumes consisting of more than 200 species. The term under-exploited wild Vigna species has been attributed to some Vigna species of legumes that have not yet been domesticated. They do not possess commercial names since they have not got a common popular use by people or groups of people. Very few domesticated legumes species exist with more than one hundred (100) wild species under-exploited despite global food demand. A recent study explored farmers’ perceptions, preferences, and possible utilization of some wild Vigna species of legumes through quantitative and qualitative surveys conducted in a mid and high altitude agro-ecological zones in Tanzania to obtain the opinion of 150 farmers about wild legumes and their uses.
  • 1452
  • 30 Oct 2020
Topic Review
Plant-Based Milk Production
Growing concerns about the environmental impacts, healthiness, and ethical implications of eating animal-based products, such as meat, eggs, and milk, has led to an increase in demand for plant-based alternatives.  Plant-based milk substitutes can be created using two main approaches. First, certain oil-rich plant tissues (such as almonds, cashews, coconut flesh, flaxseeds, or soy beans) can be converted into colloidal suspensions using size-reduction and isolation techniques (such as soaking, grinding, enzyme-treatment, filtration, and centrifugation).  Second, plant-based oils (such as corn, flaxseed soybean, or sunflower oil) can be homogenized with water in the presence of plant-based proteins (such as pea, legume, or soy proteins), polysaccharides (gum arabic or beet pectin), phospholipids (such as soy or sunflower lecithin), or saponins (such as quillaja saponin) to create an oil-in-water emulsion with similar characteristics to bovine milk.   
  • 1132
  • 28 Oct 2020
Topic Review
Cytokinins in Horticultural Fruit Crops
Cytokinins (CKs) are a chemically diverse class of plant growth regulators, exhibiting wide-ranging actions on plant growth and development, hence their exploitation in agriculture for crop improvement and management. Their coordinated regulatory effects and cross-talk interactions with other phytohormones and signaling networks are highly sophisticated, eliciting and controlling varied biological processes at the cellular to organismal levels. In this review, we briefly introduce the mode of action and general molecular biological effects of naturally occurring CKs before highlighting the great variability in the response of fruit crops to CK-based innovations. We present a comprehensive compilation of research linked to the application of CKs in non-model crop species in different phases of fruit production and management. By doing so, it is clear that the effects of CKs on fruit set, development, maturation, and ripening are not necessarily generic, even for cultivars within the same species, illustrating the magnitude of yet unknown intricate biochemical and genetic mechanisms regulating these processes in different fruit crops. Current approaches using genomic-to-metabolomic analysis are providing new insights into the in planta mechanisms of CKs, pinpointing the underlying CK-derived actions that may serve as potential targets for improving crop-specific traits and the development of new solutions for the preharvest and postharvest management of fruit crops. Where information is available, CK molecular biology is discussed in the context of its present and future implications in the applications of CKs to fruits of horticultural significance.
  • 1054
  • 27 Aug 2020
Topic Review
Vegetable Wastes and Byproducts
Agri-food industries generate enormous amounts of fruit and vegetable processing wastes, which opens up an important research area aimed towards minimizing and managing them eciently to support zero wastes and/or circular economy concept. These wastes remain underutilized owing to a lack of appropriate processing technologies vital for their ecient valorization, especially for recovery of health beneficial bioactives like dietary fibers. Dietary fiber finds wide applications in food and pharmaceutical industries and holds high promise as a potential food additive and/or as a functional food ingredient to meet the techno-functional purposes important for developing health-promoting value-added products. Based on this, the present review has been designed to support ‘zero waste’ and ‘waste to wealth’ concepts. In addition, the focus revolves around providing updated information on various sustainability challenges incurred towards valorization of fruit and vegetable wastes for extraction of health promoting dietary fibers.
  • 1039
  • 26 Oct 2020
Topic Review
Machine Learning for Plant Breeding/Biotechnology
Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity, classification of plant genotypes, analysis of yield components, yield stability analysis, assessment of biotic and abiotic stresses, prediction of parental combinations in hybrid breeding programs, and analysis of in vitro-based biotechnological experiments are mainly performed by classical statistical methods. Despite successful applications, these classical statistical methods have low efficiency in analyzing data obtained from plant studies, as the genotype, environment, and their interaction (G × E) result in nondeterministic and nonlinear nature of plant characteristics. Large-scale data flow, including phenomics, metabolomics, genomics, and big data, must be analyzed for efficient interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with "multiple-independent variables versus multiple-dependent variables" nature. Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear and complex data in both classical plant breeding and in vitro-based biotechnological studies. High interpretive power of machine learning algorithms has made them popular in the analysis of plant complex multifactorial characteristics. The classification of different plant genotypes with morphological and molecular markers, modeling and predicting important quantitative characteristics of plants, the interpretation of complex and nonlinear relationships of plant characteristics, and predicting and optimizing of in vitro breeding methods are the examples of applications of machine learning in conventional plant breeding and in vitro-based biotechnological studies. Precision agriculture is possible through accurate measurement of plant characteristics using imaging techniques and then efficient analysis of reliable extracted data using machine learning algorithms. Perfect interpretation of high-throughput phenotyping data is applicable through coupled machine learning-image processing. This entry shows how nonlinear machine learning algorithms can be used in different branches of classical plant breeding and in vitro-based methods. An idea is provided at the end of the entry that shows how coupled image processing-machine learning (especially deep CNN) could be used to identify the ploidy level of plants. It could be used in laboratories without flowcytometry equipment and/or in plant species without an established chromosome counting protocol.
  • 1028
  • 16 Feb 2021
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