Pre and Post Harvesting using Deep Learning Techniques: A comprehensive study

The paper aims to provide a comprehensive view about the use of deep learning Pre-harvest and Post – Harvest management in viticulture. Traditional approach targets manual aid in Pre and Post-harvest Quantitative and Qualitative analysis of post-harvest and pre-harvest management. The traditional approach has its disadvantages like an undue time consuming, Chances of manual errors and Recordkeeping maybe a voluminous task. The observational study which aims to eliminate technical discrepancies by using deep learning ensures benefits like AI-assisted algorithm and image processing ensures accuracy in data reading and analysis, automatic fruit quality monitoring reduces "Farm–to–Table" time provides better marketing quality for grapes and Optimum utilization of supply chains. We’ll look at how machine literacy can be used in current vineyard operations and processes to yield industry-applicable quality. The recurrent neural network is a deep knowledge classifier that may be used to make a system that can identify grapes rested on their quality. Deep learning networks like AlexNet, GoogleNet, and VGG16 (Very Deep Convolutional Networks for Large-Scale Picture Recognition) could enhance image segmentation and classification of grape bunches while also resolving approach negligence. The study concludes by demonstrating how deep literacy can be used for pre-and post-harvest operations in the viticulture industry.