Artificial neural networks in vegetables: A comprehensive review

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. In this communication an all inclusive review of ANN for predictive modeling, analysis that play crucial role 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, has been discussed. The objective of this write-up is to provide all published literature related to ANN modeling in vegetables at one single stop, which would be very valuable for agriculturalists, academicians, researchers, scientists and students, so that they can follow a suitable methodology according to their exact requirements for conducting research.