Bu çalışmada, Türkiye İstatistik Kurumu (TUİK) verilerine göre Türkiye'nin ve on tarım bölgesinin; son 10 yıla (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) ait tarımsal mekanizasyon düzeyi gösterge değerleri (kW ha -1 , traktör sayısı 1000 ha -1 , ha traktör -1 , makina traktör -1 ) belirlenmiş ve gelecek yıllar (2020-2030) için kW ha -1 değeri trend analizi yardımıyla tahmin edilmiş ve değerlendirilmiştir. Türkiye'nin on tarım bölgesi ve Türkiye genelinde yıllara göre mekanizasyon düzeyi göstergelerinden kW ha -1 ve traktör 1000 ha -1 değerleri artmış, makina traktör -1 değeri ise azalmıştır. kW ha -1 değeri; en yüksek Ege Bölgesinde (2.86-3.72 kW ha -1 ), en düşük Doğu Karadeniz Bölgesinde (0.35-0.44 kW ha -1 ) oluşmuş, Türkiye genelinde son on yılda ortalama %3.22 artış göstermiş ve 1.67-2.22 kW ha -1 olarak gerçekleşmiştir. Gelecek yıllar (2020-2030) için kW ha -1 değeri; Türkiye ve tüm tarım bölgelinde yıllara göre artış göstereceği tahmin edilmiştir. Gelecek yıllar (2020-2030) için ortalama artış değerleri %0.89-3.18 arasında gerçekleşeceği öngörülmektedir. kW ha -1 değeri; Türkiye geneli için 2020 yılında 2.27, 2025 yılında 2.55 ve 2030 yılında 2.84 olması tahmin edilmektedir. Makina traktör -1 oranındaki azalma; on yıllık süreçte makina sayılarındaki artış oranlarının traktör sayılarındaki artış oranlarından daha az olmasından kaynaklanmaktadır.

Today, image processing techniques are frequently used in irrigation, fertilization and spraying applications in order to increase agricultural input efficiency and product quality. In this study, the relationship between the image and weight of soybeans was investigated. For this purpose, some image processing applications were carried out on the images of soybeans grown with the deficit irrigation (100%, 75, 50 and 25) method. In the study, the relationship between the weight of soybeans and the number of pixels occupied on the images was 88.78%. The weights belonging to the displayed soybean grains decreased from 100% watered to 50% watered, in the 25% irrigated area, it increased again. The 25% irrigated case created significant stress for soybeans. However, as in some plants, this situation caused an increase in grain weight in soybeans.

The chemical analysis of CaCO3 contents of soils provide information about not only efficiency of soil for supplying plant with nutrients but also identification of factors affecting this efficiency in the soil. The aim of the current experiment was to develop new methods based on sensors and compare with conventional Scheibler method. The CO2 liberated by the action of HCl on CaCO3 content of the soil in a reaction bottle and an accumulation chamber was determined by pressure sensor and CO2 sensor, respectively. The CaCO3 contents of soils were estimated using the regression equation of standard curves. The CaCO3 contents of soils obtained using Scheibler calcimetric, pressure and CO2 sensor methods ranged from 0.20 to 54.74%, 0.25 to 54.10% and 0.55 to 53.05%, respectively. On the basis of linearity of calibration curve and high correlation with Scheibler calcimetric method it can be said that developed pressure and CO2 sensor methods appears to be very useful for quantification of CaCO3 contents of soils. The pressure sensor has provided the opportunity in developing a simple and handy device with an affordable cost to measure CaCO3 contents of soils as accurately as Scheibler calcimeter when compared with CO2 sensor method. Even if there is a good relationship between Scheibler calcimetric method and CO2 sensor method the cost of CO2 sensor method may limit the use in determination of CaCO3 contents of soils. However, CO2 sensor method can be used to monitor CO2 evolved in biochemical process such as respiration and fermentation

It is aimed to determine the level of mechanization in the Southeastern Anatolia Region in order to make better production planning, increase productivity and create projections for the future in agricultural enterprises. Methods and Results: In this study, mechanization level indicator values (kW ha -1 , number of tractors 1000 ha -1 , ha tractor -1 , machine tractor -1 ) in the provinces of Southeastern Anatolia Region in Turkey were determined for the years 2010-2019 and the trends in the kW ha -1 value were predicted to using trend analysis for the years 2020-2030. The data were obtained from the Turkey Statistical Institute and the mechanization level indicators were calculated. Conclusions: Throughout the region by years, kW ha -1 and tractor 1000 ha -1 indicator values increased (0.70-0.99 kW ha -1 ; 17.45-24.63 tractor 1000 ha -1 ), but ha tractor -1 and machine tractor -1 values decreased (57.29-40.60 ha tractor -1 ; 5.17-5.04 machine tractor -1 ). The highest kW ha -1 value was in the province of Adıyaman (1.81-2.47 kW ha -1 ) and the lowest was in Diyarbakır province (0.60-0.74 kW ha -1 ). This indicator reached 0.70-0.99 kW ha -1 with an average increase of 3.70% in ten years throughout the region. An average increase of 2.47% was found in kW ha -1 value for the period 2020-2030 for the whole region. This indicator was 2.2% greater than the average of Turkey. kW ha -1 value for the whole region was estimated to be 1.02 in 2020, 1.16 in 2025 and 1.31 in 2030. The level of mechanization in the region has improved over the years, but this level of recovery rate was determined to be low. Significance and Impact of the Study:Comprehensive identification of the level of agricultural mechanization on a regional/provincial basis with current data will be able to contribute to agricultural development plans and ensure that the correct decisions are made for the future. In this way, agricultural enterprises will be able to make healthier production planning, select optimum tractor and machine sizes, increase productivity and create projections for the future.

Weed control is vital in agricultural production. Chemical control methods are generally preferred in weed control as they (1) affect quickly and (2) reduce the labour requirement. However, in conventional applications chemicals are generally applied to whole field surface. Therefore, non-targeted areas are also sprayed. This increases 1) amount of herbicide used and (2) risk of off-target chemical movement. In this study, a patch spraying system was developed to automatically detect and spray herbicides on weeds in the corn field based on weed density. In order to determine the weed regions, a digital camera was fitted in front of the tractor. The images taken using the camera were then simultaneously processed using an algorithm written in Matlab TM software. The results of the field study showed that at 4, 6 and 8 km h -1 forward speeds, application volumes decrease by 30.21%, 28.82% and 32.28%, respectively, when it is compared to the conventional application methods. It was also determined that the application accuracy rates were 80%, 81.66% and 75% respectively for 4, 6 and 8 km h -1 speeds.