The recognition of potato varieties using of neural image analysis method

Abstract

The aim of this paper was to extract the representative features and generate an appropriate neural model for classification of varieties of edible potato. Potatoes of variety the Vineta and the Denar were the empirical object of this thesis. The main concept of the project was to develop and prepare an image database using the computer image analysis software. The choice of appropriate neural model the one which will have the greatest abilities to identify the selected variety. The aim of this project is ultimately to conduct assistance and accelerate work of the expert, who classifies and keeps different varieties of potatoes in heaps.

Effect of boiling on classification performance of potatoes determined by computer vision

“…Cultivar identification of sweet potato using NIR hyperspectral imaging and FT-MIR microspectroscopy revealed that two cultivars may be distinguished with 100% correctness [16]. Identification of two cultivars of potato tubers using neural image analysis based on aspect factors, geometric and color features was successfully performed with the quality of testing of above 0.99 by Przybył et al [17]. In addition to cultivar identification, image analysis based on morphological features was also applied for the detection of potatoes with an irregular shape [18], the detection of potato tubers with damages, diseases and defects [19][20][21][22], the prediction of shape parameters and mass of normal and deformed potatoes [7], the classification of the potato tubers based on moisture content [23], the prediction of changes in moisture content and color of sweet potato during drying [24], the prediction of texture and color in cooked and cooked freeze-dried rehydrated potatoes [25].…”

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

“…The training set consisted of 1800 randomly selected data, and it was divided proportionally 2:1:1 into In order to create the classification neural models, a neural network simulator implemented in Statistica v. 10 package was used [1,[18][19][20]. The most important stage of the ANN generation was the preparation of the training files containing encoded selected representative properties, constituting the empirical basis for the classification [20][21][22][23]. For this purpose, four numerical input variables and one nominal output variable were determined, which resulted from the nature of the formulated scientific problem and represented the characteristic parameters of the process examined.…”

The Effectiveness of the Application of a Chemical Agent (Dressing) to Seed Potatoes by Means of an Innovative Valve Enabling Intermittent Flow of a Liquid

“…Thus, the retention ratio R can be calculated. A control device (6) was specially designed to control the test facility. The delay of the switching on time of the solenoid valves and the duration of the solenoid valves switching on time are freely programmable in the control device.…”

“…A laboratory facility for assessment of the degree of coverage by means of the weight method: 1-conveyor belt, 2-dressing (spray) chamber, 3-guide channel, 4-optical sensor, 5-innovative solenoid valves, 6-electronic control system, 7-working liquid pump, 8-working liquid pressure control valve, 9-air pressure reducing valve, 10-working liquid tank, 11-working liquid pressure gauge, 12-potato tuber container.A control device(6) was specially designed to control the test facility. The delay of the switching on time of the solenoid valves and the duration of the solenoid valves switching on time are freely programmable in the control device.Agriculture 2020, 10, 85 5 of 14 Agriculture 2019, 9, x FOR PEER REVIEW 5 of 14…”