Artificial neural networks in vegetables: A comprehensive review
Journal Title: Scientific Journal of Crop Science - Year 2013, Vol 2, Issue 7
Abstract
Artificial neural networks(ANN) are implemented in a large number of applications of science and technology as the technique has become very popular and accepted tool for researchers and scientists. ANN renders realistic advantages such as real time processing, adaptability and training potential over conventional methodologies. We present an all inclusive review of ANN for predictive modelling, analysis and discuss the crucial role that they play in assessment of extensive range of vegetables, viz., asparagus, alfalfa sprouts, anise,basil, beans, beetroot, bell pepper, broccoli, cabbage, carrot, capsicum,celery, chickpea, chilli pepper, corn, cruciferous sprouts, cucumber, garlic, ginger, herb, jalapeno, lemon grass oil, lentils, maize, marjoram, mushroom, okra pods, onion, oregano, parsnip,peas, pepper, potato, potato chips, pumpkin, rhubarb, rosemary, soybean,spinach, thyme, turnip and walnut. The objective of this communication is to provide all published literature related to ANN modelling in vegetables at one single stop, which would be very valuable for agriculturalists, academicians, researchers, scientists and students, so that they can follow an suitable methodology according to their exact requirements for conducting research.
Authors and Affiliations
S. Goyal| Member, IDA, New Delhi, India.
Keywords
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- EP ID EP1010
- DOI 10.14196/sjcs.v2i7.928
- Views 478
- Downloads 23
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