Comparative Analysis of Ensemble Learning Algorithms for Pumpkin Seed Prediction
Abstract
Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. This research paper presents a comparative analysis of three ensemble learning algorithms, namely Bagging, AdaBoost, and LogitBoost, for the prediction of pumpkin seed quality. The study aims to determine the algorithm that provides the most accurate and reliable predictions. Experimental results are obtained using a carefully curated dataset, and various evaluation metrics including accuracy, precision, and recall are utilized to assess the performance of each algorithm.
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Introduction
The really specialized errand of agrarian creation in the field of seed creation is to get contingent seeds that meet public and worldwide quality principles. One of the significant phases of developing agrarian seeds is their post-gather handling, which incorporates the drying system, which is the fundamental and one of the best strategies for putting away and handling horticultural unrefined components. It is feasible to expand the proficiency of the innovation of post-reap treatment of pumpkin seeds by deciding the conceivable outcomes of utilizing a precise way to deal with tackling the issues of carrying out measures and means, distinguishing the component of working and fostering its viability.
Pumpkin has a place with Cucubitaceae family. Assurance of the actual traits of farming items is extremely huge for plan of post-reaping innovations and expectation of a few fundamental boundaries and qualities accuratel[1][2]. Actual traits like mathematical mean width, sphericity, grain direction, surface region, grain volumetric and explicit weight, thickness, porosity and variety are utilized to configuration cycles and hardware for item handling, transportation, screening, stockpiling and drying-like cycles.
Conclusion
Overall, this study contributes to the field of agricultural research by evaluating and comparing the performance of different ensemble learning algorithms for pumpkin seed prediction. The findings can be utilized by seed producers, farmers, and researchers to enhance seed quality control and optimize pumpkin crop yields.
Further research can explore other ensemble learning algorithms and incorporate additional features to enhance the predictive performance for pumpkin seed quality prediction. Additionally, investigating the interpretability of the ensemble models and identifying the most important features for accurate predictions could provide valuable insights for pumpkin seed quality assessment.