This study aimed to develop a multi purpose digger for harvesting root crops (potato and peanut), separating and transporting them over soil surface with minimum losses, mechanical damage and cost. Root crops digger was developed by adding a successful vibrating separating mechanism that should based on separating root crops with minimum losses and damage. The developed digger was tested at three levels of forward speeds (1.8, 2 and 2.6 km/h), for potato, (1.4, 1.8 and 2.3 km/h), for peanut and three different tilt angles (12ْ , 18 and 24ْ). The experiments were carried out during two successful agricultural seasons of 2007 for peanuts at El Assasin country, El Sharkia Governorate and 2008 for potato at Manzala city, El Dekahlia governorate. From the obtained results, it was cleared the proper conditions to operate the developed digger were 22 cm harvesting depth, 2.6 km/h forward speed and 0.31 rad (18ْ) tilt angle for potato crop and 15 cm harvesting depth, 2.3 km/h forward speed and 0.21 rad (12ْ) tilt angle. The cost of harvesting using the digger was 91.55 L.

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Diabetic retinopathy (DR) is one of the leading causes of vision loss. It causes neovascularization with blocking the regular small blood vessels. Early detection helps the ophthalmologist in patient treatment and prevents or delays vision loss.In this work, multifractal analysis has been used in some details to automate the diagnosis of diabetic without diabetic retinopathy and non-proliferative DR. Concerning using number of multifractal geometrical methods, as a necessary second step the enforcement of the sophisticated artificial neural network has been consultant in order to improve the accuracy of the obtained results.Patients and methods: Thirty normal cases' eyes, 30 diabetic without DR patients' eyes and 30 non-proliferative diabetic retinopathy (mild to moderate) eyes are exposed to optical coherence tomography angiography (OCTA) to get image superficial layer of macula for all cases. These images were approved in Ophthalmology Center in Mansoura University, Egypt, and medically were diagnosed by the ophthalmologists. We extract the most changeable features that associated to the morphological retinal vascular network alternations. The seven extracted features are related to the multifractal analysis results, which describe the vascular network architecture and gaps distribution. A supervised Artificial Neural Network (ANN) is used to classify the images into three categories: normal, diabetic without diabetic retinopathy and non-proliferative DR. Results:The human retinal blood vascular network architecture is found to be a fractal system. Multifractal geometry describes the irregularity and gaps distribution in the retina. We extracted seven features from the studied images. The features were the generalized dimensions D 0 , D 1 , D 2 , α at the maximum f (α) singularity spectrum, the spectrum width, the spectrum symmetrical shift point and lacunarity. The ANN obtains a single value decision with classification accuracy 97.78%, with minimum sensitivity 96.67%. Conclusion:Early stages of DR could be noninvasively detected using high-resolution OCTA images that were analysed by multifractal geometry parameterization and implemented by the sophisticated artificial neural network with classification accuracy 96.67%. This approach could promote risk stratification for the decision of early diagnosis of diabetic retinopathy.

A small grading machine was developed, fabricated and evaluated technically and economically acts by using cylinder system of rod for grading system for crops such as Baladi eggplant variety be used in small horticultural holdings, small grading stations and small factories. Series of experiments were carried out to study effect of some parameters such as rotary speed of grading unit (0.31; 0.41; 0.59) m/sec and slope angle of grading unit (0.0; 1.0; 2.0; 3.0 deg.) on grading machine capacity (ton/h), grading efficiency (%), mechanical damage of eggplant (%), specific energy consumption (kW. h/ton) and total operation cost (LE / ton) to evaluate the grading prototype. The optimum results were obtained at rotary speed 0.41 m/sec and grading slope angle 2.0 degree. At above variables the grading efficiency recorded 84.6 %, damage index of 2.71%, specific energy consumption of 0.32 kW. h/ton and total productivity cost for one ton was 5.62 LE / ton for Baladi eggplant variety.