Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use machine learning algorithms to determine the above relationship. Algorithms include multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), cubist, random forest (RF), and artificial neural networks (ANN). Machine learning made it possible to predict soil penetration resistance from huge sets of environmental data obtained from onboard sensors on satellites and other sources to produce digital soil maps based on classification and slope, but whose output must be verified if they are to be trusted. This review presents soil penetration resistance measurement systems, new technological developments in measurement systems, and the contribution of precision agriculture techniques and machine learning algorithms to soil penetration resistance measurement and prediction.

The relationship between the power consumed in the engine and the power take-off shaft of a maize silage harvester is critical to understanding the efficiency and performance of the harvester. The power consumed in the engine directly affects the power available for use on the P.T.O shaft, which...

Cavitation, a physical phenomenon that detrimentally affects pump performance and reduces pump life, can cause wear on pump elements. Various engineering methods have been developed to identify the initiation and full development of the cavitation process. One such method is the determination of the net positive suction head (NPSH) through a 3% decrease in total head (Hm) at a constant flow rate. In radial pumps, commonly used in agricultural irrigation and industry, cavitation conditions result in a sudden drop in the Hm-Q curve, making it challenging to detect the 3% Hm value drop. This study differs from others in the literature by modelling NPSH, noise, and vibration levels using three machine learning models, specifically artificial neural networks (ANN), support vector machines (SVM), and decision tree regression (DTR). The best-performing model predicts NPSH, noise, and vibration levels corresponding to a 3% decrease in Hm level. The present study determined the NPSH values of a horizontal shaft centrifugal pump at different flow rates and constant operating speed, and the vibration and noise levels were measured for these NPSH values. For each of the NPSH, noise, and vibration levels, ANN, SVM and DTR models were created. The performances of these models were evaluated using criteria such as root mean squared error (RMSE), Mean Absolute Error (MAE) and mean absolute percentage error (MAPE). In addition, Taylor and error box diagrams were created. The ANN model and DTR yielded high accuracy predictions for NPSH values (R2 = 0.86 and R2 = 0.8, respectively). The ANN model provided the best prediction performance for noise and vibration levels. By entering the level of 3% drop in the Hm value of the pump as external data input to the ANN model, NPSH3, noise, and vibration levels were determined. The ANN models can be effectively employed to determine NPSH3, noise, and vibration levels, particularly in radial flow pumps, where detecting 3% reductions in manometric height value is challenging.

Comparison of Strip Tillage Systems for Sillage Maize Production in Middle Anatolia

Günümüzde yem bitkileri üretim alanlarının artırabilmesi için mevcut yem bitkileri çeşitliliğinin artırılması ve ekim nöbetinde kullanılmaları zorunludur. Bu alanlarda ekolojik şartların elverdiği ölçüde ekonomik üretim modelini geliştirmek gerekmektedir. Bu çalışma 2018 yılında, Konya Selçuk Üniversitesi Ziraat Fakültesi Sarıcalar Uygulama ve Araştırma Çiftliğinde yem bitkilerinin çeşitliliğinin artırılması amacı ile yürütülmüştür. Çalışmada; İtalyan çiminin mekanizasyon özelliklerinin ve enerji verimliliğinin belirlenmesi amaçlanmıştır. Üretiminde kullanılan makine ve ekipmanların yakıt tüketimleri, toplam enerji girdi ve çıktıları ile enerji verimliliği hesaplanmıştır. Araştırma sonuçlarına göre; En fazla yakıt tüketimi hasat grubu makinelerde 43.2 l ha-1 ile balya makinesinde, toprak işleme grubu makinelerinde ise 17.2 l ha-1 ile pullukla çalışmada tespit edilmiştir. Toplam enerji çıktısı 81572.40 MJ ha-1 ve toplam enerji girdisi 34197.97 MJ ha-1 olarak hesaplanmış ve bunlara bağlı olarak net enerji verimliliği 47374.43 MJ ha-1, enerji oranı 2.39 ve enerji verimliliği de 0.14 kg MJ-1 olarak bulunmuştur. Özgül enerji olarak tanımlanan bir kg ürünün elde edilmesi için gerekli enerji miktarıda 7.31 MJ kg-1, Net enerji veriminin toplam enerji girdisine oranını ifade eden net enerji oranı da 0.58 olarak hesaplanmıştır. Enerji girdileri içinde doğrudan enerji %45.62, dolaylı enerji %54.38 yenilenemeyen enerji %65.38 ve yenilenebilir enerji ise %34.62 paya sahip olduğu tespit edilmiştir.

Tanelik Mısır Üretiminde Çift Sıra Ekim Yöntemlerinin Verim ve Verim Parametrelerine Etkisi