العنوان ELECTRONIC VISION AND USES IN AUTOMATIC HANDLING OF AGRICULTURAL MATERIALS / المؤلف ABDEL ZAHER, ALSHAYMAA MOHAMED RAMZY. هيئة الاعداد باحث / ALSHAYMAA MOHAMED RAMZY ABDEL ZAHER مشرف / Mohammed Nabil El Awady مشرف / Essam Ahmed Soliman El-Sahhar مناقش / Mohamed Fayed Abdel-Fattah Khairy تاريخ النشر 2019. عدد الصفحات 65 P. : اللغة الإنجليزية الدرجة الدكتوراه التخصص الهندسة الزراعية وعلوم المحاصيل تاريخ الإجازة 1/1/2019 مكان الإجازة جامعة عين شمس - كلية الزراعة - قسم الهندسة الزراعية الفهرس | Only 14 pages are availabe for public view |

AbstractThe main results in this study can be summarized in the following points: Physical and mechanical properties of fruits: The mean fruit masses are 130 g, 270 g, and 100 g for apples, navel oranges, and tomatoes, respectively. The mean volumes are: 175 cm3, 350 cm3, and 75 cm3 for apples, navel oranges, and tomatoes, respectively. Density means are 1050 kg/m3, 850 kg/m3 for apples, navel oranges, and tomatoes respectively. Mean diameters are 5 cm, 7.25 cm, and 4.75 cm for apples, navel oranges, and tomatoes, respectively. Sorting-machine efficiency: Tomatoes sorting-efficiency was 96 % at the lowest belt speed and increased to 100 % at the mean belt speed, then decreased to 94 % at the highest speed, because of the fast motion of belt with the slow response of RGB system. Apples sorting efficiency was 95 % at the lowest belt speed and increased to 100 % at the mean belt speed then decreased to 92 % at the highest speed, because of the last reason in tomatoes. Navel oranges sorting efficiency was 95 % at the lowest belt speed and increased to 100 % at the mean belt speed then decreased to 80 % at the highest speed, also because of the last reason in tomatoes. Rates of performance (RP): For manual sorting, RP values are arranged in ascending order from 22.5 to 50.1 kg/h for tomatoes, from 27.4 to 60.1 kg/h for apples, and from 58.4 to 134.3 kg/h for navel oranges. Automatic sorting as arranged in ascending order from 25.4 to 54.8 kg/h for tomatoes, from 30.2 to 71.5 kg/h for apples, and from 61.2 to 142.7 kg/h for navel oranges. Dimensional analysis of the productivity system: We can elaborate computer-model results and curve trends for the “Matlab Program” simulation by drawing the relations between π groups, as shown in the next. Also, we can predict the trend of curves of sorting productivity by drawing the relation between π groups. Thus we can approximately estimate the machine productivity values by manipulating the groups. Computer-aided study in drawing relationship between productivity and belt speed: The relation between pi groups gave the same trend of curves as the results of the “Matlab Program” by using the following functioned equation: Pr = f (v, T, λ, ρ; m), Where: Pr = the productivity of sorting color, kg/s. v = belt speed, m/s. T = delay time of servo motor, s. m = mean mass of fruit, gm. λ = red component ratio to the RGB white-base “73% for red apples, 38% for green apples, 100% for navel oranges, 100% for tomatoes”. ρ = density of fruit, kg/m3. The resulting π-groups were: (P_r T )/m = f {(v T ρ^(1⁄3))/m^(1⁄3) , λ} Cost analysis: The sorting process cost by using the developed machine is as follows: It was more expensive in sorting tomatoes by the developed machine compared with manual sorting by 25 % because of the small fruits volume so the number of catching fruits will be big, so thus time will increase which leads to increasing cost It reduced the cost of fruits sorting by 4% of sorting apples. And it reduced cost by 52% as compared with manual method in navel oranges sorting. Recommendations: from experiments, we have to determine the optimum design and operating factors for the developed machine to give the optimum productivity. These factors are: Optimum belt speed 0.8 km/h. Optimum distance between the gate and the sensor 12 cm. Optimum delay time of servo motor 1.3 second. Scientific contribution: A sorting system has been developed and constructed in a small model which resulted in increasing productivity from 134 kg/h to 142 kg/h with saving in cycle time of 21.5 %. “Arduino Technology” has been successfully utilized, with locally available components for accurate and convenient sorting and handling. We can collect the factors that affect the sorting system in dimensionless groups in the following form: (P_r T )/m = f {(v T ρ^(1⁄3))/m^(1⁄3) , λ} (Symbols are as defined in the text) We can predict the results and curve trend of productivity by using “Matlab Program” and the pi groups elaborated herein. |