Artificial neural networks in vegetables: A comprehensive review

Abstract

Artificial neural networks(ANN) are implemented in a large number of applications of science andtechnology as the technique has become very popular and accepted tool forresearchers and scientists. ANN renders realistic advantages such as real timeprocessing, adaptability and training potential over conventionalmethodologies. We present an all inclusive review of ANN for predictivemodelling, analysis and discuss the crucial role that they play in assessmentof 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 grassoil, 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 toprovide all published literature related to ANN modelling in vegetables at onesingle stop, which would be very valuable for agriculturalists, academicians,researchers, scientists and students, so that they can follow an suitablemethodology according to their exact requirements for conducting research.