Analysis of Improve the Quality of Grapes in India Using Machine Learning Algorithms

Agriculture is by far the most common profession in India. The last stage of crop production is very important to a farmer’s overall performance, despite the fact that it is typically followed by pre-harvest losses that may amount to as much as fifty percent of the crop. It is possible that if this waste were reduced, global food production as well as the income of farmers in general would increase. There may be actions that take place just before harvest that have a major influence on the total production of the crop. The purpose of this study is to shed light on machine learning in agriculture by conducting a comprehensive review of the most recent scholarly literature based on keyword combinations of “machine learning” along with “Properties of Soil,” “Properties of Water,” “Effect of Climatic and weather changes on agriculture,” and “Nutrient deficiencies and their toxic effects are observed during the harvesting of grapes,” etc. The review is based on keywords that were chosen based on the combination of “machine learning” with In recent years, machine learning has found applications in a wide variety of fields, including but not limited to computers, categorization, bioinformatics, marketing, medical diagnosis, game playing, healthcare, and industry. The “Naive Bayes Classifier, K Means clustering, support vector machine (SVM), artificial neural networks (ANN), decision trees, and random forest: are examples of machine learning techniques that are commonly used in research. Data collection, preparation of datasets, feature extraction, pre-processing, feature selection, selecting and implementing appropriate machine learning algorithms, and performance assessment are all part of machine learning. These technologies are utilized to acquire photos, proceed with additional pre-processing of the images or data set, and ultimately categorize the high quality grapes for export.