Estimation and Classification of Physical Parameters Pumpkins (Cucurbita pepo L.) Crop S by Soft Computing Tecniques

Abstract

Determining the seed type is very important for the correct indentification of genetic material. Some plant seeds can not be classified based on their visual diversity or small size by experts. Therefore, in this study was to develop a simple, accurate and rapid using different soft computing tecniques that estimates physical parameters for pumpkin seeds. The current investigation was devoted to determining some properties, such as physical dimensions, surface area, sphericity, density, rupture energy of pumpkin seeds. The methods using in this study are; (1) Multilayer perceptron (MLP); (2) Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Different statistic parameters such as coffecient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) are used to evaluate performance of the methods. These selected the best models predicted for plant seeds which can be used in the soft computing tecniques determined alternative approach to estimating the physical properties of estimation and clasification pumpkin seeds.

Plant phenotyping relevance

カボチャ種子の物理形質を推定・分類するソフトコンピューティング手法を開発し、誤差指標で性能評価しており、形質取得・推定法が中心である。

Code and data availability

The article describes pumpkin seed physical property measurements and MLP/ANFIS modeling, but no blocks contain any data availability statement, public dataset deposit, author code repository, or trained model release. No paper-specific public asset is identified.

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