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Analysis of the strength of an innovative design of an organic farming potato harvester
Abstract Small organic farms still use potato lifters for harvesting. This harvesting technology involves a lot of work because potatoes need to be picked manually. The aim of this study was to design an innovative organic farming potato harvester aggregated with a 38 kW tractor and to analyse its strength with the finite element method (FEM). The research assumption was to fit the innovative construction with a potato basket in order to minimise the labour consumption of organic potato cultivation. The project involved analytical calculations of the strength, which were followed by the design of a CAD model and a detailed strength analysis with the FEM. Autodesk Inventor and Femap were the programs used to aid the design of the machine. The designed model had no nodes where stresses would be greater than 32% of the maximum allowable stress in the material structure and 43% of the maximum allowable stress in the structure of welds. The innovative design of the potato harvester developed in this study can be used with all tractors (farm and orchard tractors) equipped with a three-point linkage.
Predicting Fruit’s Sweetness Using Artificial Intelligence - Case Study: Orange
The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.