ENHANCING TOMATO VARIETY SELECTION AND YIELD PREDICTION MODEL USING ADVANCED MACHINE LEARNING TECHNIQUES - R., Usha Devi; Selvakumari, N. A. Sheela (ShodhKosh: Journal of Visual and Performing Arts, 2024)
One important agricultural practice that greatly contributes to the world's food supply is the growing of tomatoes. However, choosing the right tomato varieties and accurately estimating their yields are difficult undertakings that depend on several variables, such as crop statistics, climate, and geography. Advanced machine learning approaches can be used to improve prediction accuracy, feature selection, and preprocessing to overcome these problems. To optimize tomato variety selection and yield prediction, this work investigates an integrated approach that uses improved preprocessing approaches for outlier and missing information, an advanced feature selection method, and a hybrid algorithm.