The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.

Purpose of this work is to increase the production efficiency in agricultural field by developing a mobile autonomous robot which has the capability of processing and monitoring field operations like spraying remedies for precision farming, fertilization, disease diagnosis, yield analysis, soil analysis and other agricultural activities. Here, major constraints are reliability and durability against field conditions versus lowering unit cost of robot for high volume manufacturing. Another design goal is using domestic resources for carrier platform, circuit boards etc. or integrating common production parts with designed or domestically available parts. Other aims of the work are all devices on the network will be able to communicate over Environmental Agriculture Informatics Applied Research Center (TARBIL) cloud services and application software will be able to transfer data to farmers' mobile devices, tractors and farming vehicles. Thus, it is aimed to decrease co-invested enterprises costs to minimum level. This work is separated into two main stages. The first stage: Integration of drivers, actuators, control system, communication system, energy management system, task management system and sensors on a suitable platform according to these criteria. The second stage: Integration of task management with TARBIL system.

A current-mode universal filter using a pair of four terminal p-type active current conveyors (CFCCII_p) is presented. The filter can simultaneously realise lowpass, highpass and bandpass responses. Realisation of the notch response does not require additional active elements. All the passive elements are grounded and the circuit has low sensitivity.

A novel universal current-mode active filter with four inputs and one output is presented, which contains only three dual-output current conveyors, two grounded capacitors and two grounded resistors. The circuit is very advantageous from an integration point of view. The proposed active filter realises all filter responses without any matching conditions at high impedance output and has low passive and active sensitivities.

This paper addresses the multiplication of one data sample with multiple constants using addition/subtraction and shift operations, i.e., the multiple constant multiplications (MCM) problem. The MCM problem finds itself and its variants in many applications, such as digital finite impulse response (FIR) filters, linear signal transforms, and computer arithmetic. Although many efficient algorithms have been proposed to implement the MCM using the fewest number of operations, due to the NP-hardness of the problem, they have been heuristics, i.e., they cannot guarantee the minimum solution. In this work, we propose an exact algorithm based on the breadth-first search that finds the minimum number of operations solution of mid-size MCM instances in a reasonable time. The proposed exact algorithm has been tested on a set of instances including FIR filter and randomly generated instances, and compared with the previously proposed efficient heuristics. It is observed from the experimental results that, even though the previously proposed heuristics obtain similar results with the minimum number of operations solutions, there are instances for which the exact algorithm finds better solutions than the prominent heuristics.