In modern agriculture, which is characterised by dynamic field environments, challenges are faced in maintaining consistent application rates due to varying tractor speeds, field conditions, and certain calibration errors. Conventional control systems, which rely on slower valves, have difficulty adapting to these dynamic field conditions. By contrast, the integration of fast-acting proportional valves improves the precision and flexibility of flow rate adjustment during spraying applications. This research focused on evaluating the accuracy of spraying applications under different tractor speed conditions through field experiments and data analysis. This study involves a field sprayer with boom wings divided into right and left sections, where the flow rate of the liquid to each section is controlled by proportional valves with a 3 s full opening and closing time, dependent on speed information. Using a closed-loop control system consisting of a flow meter, proportional valve, and PLC, the valves are controlled by the PLC’s internal PID blocks. Observations reveal that as the tractor speed increases to a certain level, the system effectively adjusts the application rate close to the target value and maintains control against the changing ground speed during all field tests. The study included five different application tests, with target application rates of 100, 150, 200, 250, and 300 L ha⁻¹, with each repeated three times, resulting in a total of 15 field tests at different ground speeds. During these tests, the data were meticulously recorded every second, covering the tractor speed, flow rate, and pressure values for both right and left boom sections, along with regulator pressure, proportional valve opening rates, and application rates. The durations for each application rate were documented alongside instances within specified periods where error boundaries of ±10% were exceeded. During the total test duration of 9734 s, the actual application rate value exceeded error boundaries during only 209 s. Within the application durations, the speed variation intervals ranged from 5.10 to 10.23 km h⁻¹, 4.64 to 9.91 km h⁻¹, 3.68 to 7.89 km h⁻¹, 4.80 to 8.21 km h⁻¹, and from 4.90 to 8.69 km h⁻¹. The absolute percentage mean application errors were recorded as 2.81%, 2.68%, 2.28%, 2.14%, and 2.51% for respective application rates. Furthermore, statistically significant correlations (p < 0.01) were identified among the variables (speed, valve opening rate, flow rate, pressure) in both the right and left boom sections across all application rates.

In recent years, computer vision systems have been used in almost every field of industry. In this study, image processing algorithm has been developed by using CUDA (GPU) which is 79 times faster than CPU. We had used this accelerated algorithm in destemming process of pepper. 65 percent of total national production of pepper is produced in our cities, Kahramanmaras and Gaziantep in Turkey. Firstly, hybrid intuitionistic fuzzy algorithm edge detection has been used for preprocessing of original image and Otsu method has been used for determining automatic threshold in this algorithm. Then the multilayer perceptron artificial neural network has been used for the classification of patterns in processed images. Result of ANN test for detection direction of pepper has shown high accuracy performance in CPU-based implementation and in GPU-based implementation.

En tr Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize output power by changing the switching duty cycle of DC-DC converters. In this work, MPPT algorithm with PID optimization based on particle swarm optimization (PSO) is presented for PV systems. In order to test the success of the proposed method, the results obtained are compared with the results of conventional PID controller and P&O methods commonly used in PV systems. The work was carried out in Matlab / Simulink environment. It has been observed that the proposed control structure is more successful than other methods in both transient and steady-state situations. Maksimum güç noktası izleme (MPPT), fotovoltaik (PV) sistemlerde, DC-DC dönüştürücülerin anahtarlama darbe genişliğini (duty cycle) değiştirerek çıkış gücünü maksimize etmek için kullanılır. Bu çalışmada, PV sistemler için parçacık sürü optimizasyon (PSO) tabanlı PID ile MPPT algoritması sunulmuştur. Önerilen yöntemin başarısını test etmek amacıyla elde edilen sonuçlar, klasik PID denetleyici ve PV sistemlerde yaygın kullanılan değiştir ve gözle (P&O) yöntemlerinin sonuçları ile karşılaştırılmıştır. Çalışmalar Matlab/Simulink ortamında gerçekleştirilmiştir. Önerilen denetim yapısının hem geçici hem de kararlı durumlarda diğer yöntemlere göre daha başarılı olduğu görülmüştür.

Bu makalede öncelikle tasarlanan robot tanıtılmaktadır. PID denetleyicinin yapısı, Ziegler Nichols (ZN) yöntemi ve Parçacık Sürü Optimizasyonu (PSO) algoritması hakkında bilgi verilmektedir. Daha sonra PSO algoritmasının kontrol sistemine uygulanmasından bahsedilmektedir. Mobil robot kolunu kontrol eden Genuino 101’in üzerinde bulunan USB portundan sonuçlar alınmakta ve Matlab’ ta grafiklere dönüştürülmektedir. Mobil robot kolunun dikey ve yataydaki konumu jiroskop ve ivmeölçerler ile belirlenmektedir. Mobil robot kolun konumunda değişim meydana geldiğinde mobil robot kolunun bilek (M4 motoru) ve bilek dönüş (M5 motoru) motorlarının kontrolü yapılmaktadır. PID denetleyici parametreleri kapalı çevrim ZN yöntemi ile belirlenip sonuçlar gerçek zamanlı olarak alınmaktadır. PSO algoritması kullanılarak elde edilen PID parametreleri denetleyicilere uygulanmakta ve sonuçlar gerçek zamanlı olarak karşılaştırılmaktadır. Elde edilen sonuçlara göre PSO+PID kontrolün Klasik PID kontrole göre daha az hata yaptığı ve en kısa sürede istenen referans konumuna ulaştığı gözlemlenmektedir.