Azerbaijan is an important region for the beekeeping and honey industry. Biochemical characteristics of eleven honey samples from different regions of Azerbaijan were analyzed through measuring electrical conductivity, optical activity, moisture content, proline and diastase number. Phenolic components are analyzed by reverse-phase high-performance liquid chromatography (RP-HPLC) with UV–Vis double beam detector. Sugars profiles of samples were carried out using HPLC with a refractive index detector (RID). Total phenolic content (TPC), ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picryhydrazyl (DPPH) free radical scavenging activity were measured with the aim of gathering information on the antioxidant power of samples. Biochemical and antioxidant characteristics are also supported with melissopalynological data. Conductivity ranged between 0.32 and 0.82 mS cm⁻¹, moisture 14.52 and 20.78%, proline 314.34 and 1077.21 mg kg⁻¹, diastase 13.05 and 22.19. Azerbaijan honeys exhibited levorotatory properties. Total phenolic content varied from 18.82 to 154.75 mg gallic acid equivalent per 100 g honey. The results confirm that physico-chemical and biologically active properties of Azerbaijan honeys are affected by the flora and geographical variations.

As a strategic global crop, maize productivity is directly threatened by leaf diseases such as Southern Leaf Blight and Gray Leaf Spot, making early and accurate detection crucial for food security. Artificial intelligence, particularly deep learning, provides a powerful solution for the automated classification of plant diseases from images. This study developed an intelligent system to address this challenge, utilizing the publicly available PlantVillage dataset to evaluate five leading Convolutional Neural Network (CNN) architectures: DenseNet121, InceptionV3, MobileNetV2, ResNet-50, and VGG16. The models were optimized with established techniques, including transfer learning, data augmentation, and hyper-parameter tuning, while a Soft Voting Ensemble strategy was used to enhance combined performance. Evaluation across multiple metrics showed that InceptionV3 achieved the highest test accuracy at 94.47%. However, MobileNetV2 demonstrated the strongest performance across all metrics with a 95% cumulative accuracy and proved highly efficient, making it ideal for deployment on mobile devices. These findings confirm the significant potential of deep learning for building cost-effective and efficient diagnostic systems in agriculture, ultimately contributing to the reduction of crop losses and the promotion of sustainable farming practices.

The accurate identification of diseases on apple production is an important issue due to the worldwide importance of apple production in contemporary agriculture. Identifying diseases correctly can be challenging and affects food safety and economic loss significantly. To alleviate this, deep learning approaches, and particularly Convolutional Neural Networks (CNN), have been able to provide new and reasonable options in the agricultural field. In this study, there is a hybrid model proposed, called DenseNet-ResNet-Hybrid, which brings together architectures from DenseNet and ResNet, to provide an improvement in the extraction of features together. It has been designed to fuse the inherent capabilities of DenseNet and ResNet, capturing both detail features and deeper level features in apple images, to enhance the ability to separate diseases that are overlapped with the producer's natural environment (e.g. overlapping leaves/fruits). We finally show two complete comparative experiments against two popular models (like VGG16, ResNet50, Inception-v3) under the exact same conditions to demonstrate the strength of their ability to accurately classify apple leaf diseases with consistency. We use a broader select of image types to demonstrate our work, and ultimately suggest our proposed hybrid model demonstrates competitive performance in accurate classification on apple images on the whole.

Red apricot (Prunus spp.) and Black Fig (Ficus carica L.) are two of the most commonly grown summer fruits in the Nakhchivan region. In this preliminary study, ethanol fruit extracts were compared based on their total phenolic content (TPC) and antioxidant properties. According to the Folin-Ciocalteu method, the TPC was found to be 176.20 mg GAE/100 g in red apricot and 45.30 mg GAE/100 g in fig. Their antioxidant capacities were assessed using 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. The scavenging activity, identified as the concentration of ethanol extract needed to scavenge half of the radicals (SC50), was determined to be 3.52 mg/mL for red apricot and 6.46 mg/mL for fig. Based on these two biological activity assays, red apricot exhibited higher phenolic composition and radical scavenging activity. However, further studies are required to understand whether these differences are attributed to secondary metabolites in these fruits.

Medicinal plants and their derivations are used as safe agents for the treatment of parasitic diseases. This preliminary study investigates antileishmanial activities of Peganum harmala essential oil (PHEO), Achillea millefolium essential oils (AMEO) and their combinations against Leishmania infantum promastigotes. A standard strain of L. infantum promastigote was cultured in a 96-well Novy-MacNeal-Nicolle media culture and antileishmanial activities of glucantime, PHEO, AMEO, an equal ratio of both and 80% PHEO+20%AMEO were investigated in concentrations of 10, 100, 500 and 1000 mg/mL and interval times of 24h, 48h and 72h. The results showed that greatest inhibition was observed in 50PHEO+ AMEO and lowest inhibition was seen in control group. The increased time and increased concentration significantly increased their efficiencies. The analyses showed a significant interaction between time and agents [F (10, 360)=7.84, P=0.000]. The agents showed better effects with increased time. In sum, an equal combination of PHEO and AMEO showed its potential as an antileishmanial safe structure and must be considered for future studies.