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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.
Measurements and Analysis of the Physical Properties of Cereal Seeds Depending on Their Moisture Content to Improve the Accuracy of DEM Simulation
This article presents the results of research on the influence of moisture on changes in the physical properties, i.e., the length, width, thickness, and weight, of dressed and untreated cereal seeds in order to improve the simulation process based on the discrete element method (DEM). The research was conducted on the seeds of three winter cereals, i.e., triticale, rye, and barley. The seeds with an initial moisture content of about 7% were moistened to five levels, ranging from 9.5% to 17.5%, at an increment of 2%. The statistical analysis showed that moisture significantly influenced the physical properties of the seeds, i.e., their length, width, thickness, and weight. As the moisture content of the seeds increased, there were greater differences in their weight. The average increase in the thousand kernel weight resulting from the increase in their moisture content ranged from 4 to 6 mg. The change in the seed moisture content from 9.5% to 17.5% significantly increased the volume of rye seeds from 3.10% to 14.99%, the volume of triticale seeds from 1.00% to 13.40%, and the volume of barley seeds from 1.00% to 15.33%. These data can be used as a parameter to improve the DEM simulation process.
Optimization of the Sowing Unit of a Piezoelectrical Sensor Chamber with the Use of Grain Motion Modeling by Means of the Discrete Element Method. Case Study: Rape Seed
Nowadays, in the face of continuous technological progress and environmental requirements, all manufacturing processes and machines need to be optimized in order to achieve the highest possible efficiency. Agricultural machines such as seed drills and cultivation units are no exception. Their efficiency depends on the amount of sowing material to be used and the patency of seed transport tubes or colters. Most available control systems for seed drills are optical ones whose operation is not effective when working close to the ground due to large dusting. Thus, there is still a need to provide seed drills with sensors to be equipped with control systems suitable for use under conditions of massive dusting that would shorten the time of reaction to clogging and be affordable for every farmer. This study presents an analysis of grain motion in the sowing system and an analysis of the operation efficiency of an original piezoelectric sensor with patent application. The novelty of this work is reflected in the new design of a specially designed piezoelectric sensor in the sowing unit, for which an analysis of indication errors was carried out. A seed arrangement of this type has not been described so far. An analysis of the influence of the seed tube tilt angle and the type of its exit hole end on the coordinates of the grain point of collision with the sensor surface and erroneous indications of the amount of sown grains identified by the piezoelectric sensor is presented. Low values of the sensor indication errors (up to 10%), particularly for small tilt angles (0° and 5°) confirm its high grain detection efficiency, comparable with other sensors used in sowing systems, e.g., photoelectric, fiber or infrared sensors and confirm its suitability for commercial application. The results presented in this work broaden the knowledge on the use of sensors in seeding systems and provide the basis for the development of precise systems with piezoelectric sensors.