ML Methods for Crop Yield Prediction and Estimation: An Exploration

Abstract

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity

“…of pest spry, no. of weeds spry, and eight binary categorical (0 for absence and 1 for presence) agronomical features, i.e., seed treatment, soil-type chikny loom, varieties adoption, harvest period April (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), planting in November, land irrigated, farmers' area >25 acres, and seed type, is used in the current study. Experiment is performed using Python's key library called scikit-learn (Sklearn) by Jupyter Notebook as https://scikitlearn.org/stable/supervised_learning.html.…”

“…Machine learning is viewed as innovative extension of statistics capable of dealing with the massive datasets by adding the methods from computer science to the repertoire of statistics [19]. Machine learning is categorized as advanced tools applied for the prediction of agricultural production [20][21][22][23]. According to Jeong et al [9], machine learning used latest process-based techniques as an alternative to traditional statistical modeling.…”

Insights into yield estimation of horticultural crops using machine learning algorithms

“…Using the data analytics tool R, the trials were run on soil data made up of 110 sample samples [19]. The experimental results showed that the JRIP algorithm performed better because it had advanced delicacy and a roughly 1.0 kappa statistic [20].…”

Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach

“…The optimization of machine learning algorithms has become a significant part for model deployment and got abundant attention of researchers being a core components of optimized machine learning algorithms for the massive amount of data (Sun et al, 2019). Machine learning (ML) algorithms has been categories as an advanced tool, being used for the prediction of agriculture production (Alagurajan and Vijayakumaran, 2020;Gonzalez Sanchez et al, 2014;Yadav et al, 2020). An optimized crop model is foremost need of the time is to handle the food trepidations (Elavarasan and Vincent, 2021a;Jeong et al, 2016).…”