This paper addresses the challenge of meeting increasing energy needs by assessing the potential of bioenergy as a sustainable resource option in South Central Texas. Available agricultural crop residues suitable for bioenergy production are evaluated from the 21 counties in South Central Texas Regional Water Planning Area (Region L). The residues produced and available for bioenergy are quantified according to the production areas for each field crop and tree area. Residue-to-product ratios of field crops are determined according to crop type and production quantity. Biomass potential of trees is calculated based on tree density and biomass production per tree. The results demonstrate that the potential productions of utilizable agricultural wastes are in the range of 898.7 t kt–1421.39 kt for Region L. The average annual energy potential is estimated at 19.27 PJ, and ranges between 14.36 and 24.18 PJ. The average potential biomass-based electricity production could compensate significant amount of coal-based electricity generated in the Texas and when agricultural wastes are available.

Accurate identification of cattle is essential for monitoring ownership, controlling production supply, preventing disease, and ensuring animal welfare. Despite the widespread use of ear tag-based techniques in livestock farm management, large-scale farms encounter challenges in identifying individual cattle. The process of identifying individual animals can be hindered by ear tags that fall off, and the ability to identify them over a long period of time becomes impossible when tags are missing. A dataset was generated by capturing images of cattle in their native environment to tackle this issue. The dataset was divided into three segments: training, validation, and testing. The dataset consisted of 15 000 records, each pertaining to a distinct bovine specimen from a total of 30 different cattle. To identify specific cattle faces in this study, deep learning algorithms such as InceptionResNetV2, MobileNetV2, DenseNet201, Xception, and NasNetLarge were utilized. The DenseNet201 algorithm attained a peak test accuracy of 99.53% and a validation accuracy of 99.83%. Additionally, this study introduces a novel approach that integrates advanced image processing techniques with deep learning, providing a robust framework that can potentially be applied to other domains of animal identification, thus enhancing overall farm management and biosecurity.

The aim of this study is to create a spatial database of basic values related to the level of agricultural mechanization with analyzable correlations. This will be helpful for decision-makers for producing policies to be followed in developing the agricultural structure of Kirsehir region. The agricultural mechanization status in Kirsehir was investigated thorough evaluation of determination criteria of agricultural mechanization level; mean tractor power, mean tractor power per area worked, mean area worked per tractor, mean number of tractors per 1000 ha, mean number of agricultural tools/machines per tractor, mean number of combine harvesters per 1000 ha and mean area per combine harvester. According to 10-year data encompassing 2006-2015: Mean tractor power of Kirsehir was 40.82 kW (lowest 27.47 kW in Akpinar, highest 52.38 kW in Kaman county); Mean tractor power per area worked was 0.84 kWha(-1) (lowest 0.63 kWha(-1) in cicekdagi, highest 1.03 kWha(-1) in Mucur county); Mean area worked per tractor 53.16 ha/tractor (lowest 29.88 ha/tractor in Akpinar, highest 79.32 ha/tractor in Kaman county); Mean number of tractors per 1000 ha is 22 (lowest 13 in Kaman, highest 34 in Akpinar county); Mean agricultural tools/machines per tractor is 6 (lowest 5 in Akpinar, highest 9 in cigekdag'i county); Mean number of combine harvesters per 1000 ha is 2 (lowest 1 combine harvester per 2500 ha in Kaman, highest 3/1000 ha in Akpinar county); Area worked per combine harvester is 895.3 ha (lowest 313.69 ha in Akpinar and highest 2564.5 ha in Kaman county). Agricultural mechanization level in this region with dominant rural sector was behind the level in the country. This effective database which can be updated and enriched with different aspects forms a basic model for planning-based applications and provides high-detail case monitoring. The database will positively contribute to consistency in decisions to be taken related to agricultural development of the region.

The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency.

Çalışma, Kırşehir ilinde sera işletmelerinde kullanılan sulama sularının kalitelerinin belirlenmesi amacıyla yürütülmüştür. Bu amaçla, ilde yer alan 12 sera işletmesinden sulama suyu örnekleri alınmıştır. Ayrıca, 0-30 ve 30-60 cm derinliklerinden üretim dönemi başlangıcı ve sonunda toprak örnekleri alınmıştır. Sulama suyu örneklerinde belirlenen pH değerleri 5.47-8.61, elektriksel iletkenlik (EC) ise 35-1720 dS m-1 arasında değişim göstermektedir. İşletmelerde kullanılan sulama sularının kalsiyum, magnezyum ve potasyum konsantrasyonları bakımından bir sorun oluşturmazken, işletmelerin %75’inin sulama suları yüksek düzeyde sodyum içermektedir. Sulama suyu örneklerinde karbonat iyonu bulunmazken, bikarbonat konsantrasyonları açısından örneklerin 10 tanesi orta sınıfında, 2 tanesinde ise önemsiz bulunmuştur. Klor sınıflamasına göre işletmelerin %91.67’si 1.sınıf ve %8.33’ü ise 3. sınıfa dahil olmuştur. Sülfat konsantrasyonları bakımından %90’ı 1. sınıfa, %10’u 2. sınıfa girmektedir. Sodyum Adsorbsiyon Oranı değerleri bakımından %91.67’si 1. sınıfa, %8.33’ü 2. sınıfta yer almıştır. Kalıcı Sodyum Karbonat değerleri bakımından ise sorun bulunmadığı belirlenmiştir. %Na değerlerine göre sınıflandırıldığında örneklerin %58.33’ü 1. sınıf, %33.33’ü 2. sınıf ve %8.33’ü 4. sınıf olarak belirlenmiştir. Bor konsantrasyonlarının bakımından %83.33’i 1.sınıf, %8.33’u 2. sınıf ve %8.33’ü 4. sınıfta yer almıştır. Yetiştirilen bitkiler dikkate alındığında toprak reaksiyonlarının sebze yetiştiriciliği açısından uygun, EC değerlerine bakıldığında, bazı sera topraklarının hafif ve orta tuzluluk gösterdiği belirlenmiştir. Çalışma sonucunda, sera işletmelerinde sulama suyu analizlerinin düzenli olarak yapılması ve topraktaki tuzluluk düzeyinin takip edilmesi yetiştiricilik açısından önemli görülmüştür.