A Survey on Crop Yield Prediction Using Machine Learning
Keywords:
Agriculture, Crop Prediction, Machine Learning, KNN, Decision Tree, Random Forest, Naive Bayes, SVM, Logistic Regression.Abstract
Agriculture is the science and practice of raising plants and animals. India is the second-largest agricultural nation in the world, with 60.45% of its land used for farming. In India, agriculture is one of the most common and least-paid professions. Being an agricultural nation, India's economy is heavily dependent on rising agricultural yields and agro-industrial goods. Machine learning can bring a boom in the agriculture field by changing the income scenario for optimal crop. This paper focuses on predicting the yield of the crop by applying various machine learning algorithms. Machine learning algorithms' predictions will assist farmers in selecting the best crops for their farms based on factors including soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer, and months. This paper focuses on a concise comparative work of several papers that discuss many methods for assessing crop yield. It seeks to predict agricultural yield through consideration and investigation of the datasets of previous years of the crop. The study also describes various current methods for auditing crop yield. It also includes a comparison of several algorithms and their advantages and disadvantages.
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