As salt-sensitive crops, the performance of legumes in resisting salinity should be improved through accessible, less expensive and commonly practiced alternatives. Our study investigated the potential benefits of coupling the activation of endogenous phosphate-solubilizing bacteria with calcium phosphate amendment, focusing on saline soil fertility, the activation of the plants' antioxidant defense mechanisms, and the phosphate solubilization efficiency of the PSBs in plants growing under salinity conditions. There were four factors in the experimental treatments; i) control (C), ii) two levels of salinity (NaCl) 0.3% and 0.9%, iii) two levels of calcium-phosphate (Ca-P): (21% Ca x 41%P), (48% Ca x 92% P) and iii) combined application (NaCl x Ca P), applied at the seedling stage. Salinity significantly reduced water uptake, physiological parameters and affected root growth strategy (-60%). PCA confirmed that stressed plants showed a positive association between antioxidant parameters which could related to overproduction of ROS, decreased phosphate solubilizing activity, destroyed soil and reduced plant tolerance to salt stress. Application of combined (NaCl x Ca-P) significantly improved the antioxidant response with accumulation of proteins and proline, increase in water uptake, chlorophyll pigments, maintain cell turgor, root hair density (70%), root hair length (71%), complete histological root formation, improved the physicochemical quality of the soil where EC decreased by about 20 % and pH became neutral, and increased the PSB number (80%). The use of Ca-P with salinity could reestablish physiological homeostasis, complete anatomy, and better plant growth. It also enhanced the physicochemical and biological properties of the saline soil. Interactivity in the plant-soil-rhizobacteria system could be a viable option to positively influence plant resilience against salt stress and saline soil rehabilitation.
The correct and prompt detection of leaf diseases in paddy crops remains essential for protecting agricultural output and yield stability and reducing major losses that occur during severe disease outbreaks. The widespread use of conventional centralized machine learning techniques becomes impractical for real-world agricultural applications, as their effectiveness diminishes under three main conditions: data privacy limitations, delayed transmission of information, along with reduced performance accuracy from non-independent and identically distributed (non-IID) data properties across different agro-climatic regions. This research develops a decentralized diagnostic system which combines Federated Learning with an Enhanced Pretrained Novel EfficientNet-Lite4 model designed for paddy leaf disease classification. The Paddy Doctor dataset served as training material for the system which contained 16,225 RGB images that classified 12 disease types alongside healthy categories. The proposed model reached 98.7% classification accuracy and 98.5% precision and 98.6% F1-score while maintaining a final loss of 0.21 after 100 communication rounds. The system underwent field testing which demonstrated its ability to generate accurate diagnostic results in real time in various working conditions. The study demonstrates how privacy preserving AI-based paddy disease management works in practice and recommends local agricultural institutions to build decentralized learning systems. The suggested system will give farmers direct access to field level decision support and facilitate the creation of sustainable digital pest management systems required to ensure food supply.
Hollyhock (Alcea rosea) is an important ornamental and medicinal plant. This study aimed to evaluate the genetic diversity and population structure of 28 endemic genotypes collected from various regions of Türkiye using an integrated approach combining morphological, biochemical, and molecular markers. Significant morphological variation was observed, and image analysis techniques were employed to classify flower and leaf colors. Biochemical assays revealed substantial diversity in bioactive compound levels, with total phenolic content ranging from 0.18 to 4.65 mg GAE/kg. Antioxidant activities also varied: FRAP (5.8 22.7%), CUPRAC (26.06–78.26%), and DPPH (13.6–70%). Molecular diversity was assessed using iPBS retrotransposon-based markers, with similarity indices ranging from 0.35 to 0.74, aligning with morphological and biochemical patterns. Principal Component Analysis (PCA) indicated dispersed distribution among genotypes in both two- and three dimensional plots, consistent with phenotypic variation. STRUCTURE analysis identified four distinct subpopulations based on genetic structure. Furthermore, machine learning (ML) models-Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Random Forest (RF)-were employed to predict key biochemical and morphological traits. Model performance varied, with different R² values obtained for antioxidant related traits. This is the first comprehensive study of Alcea rosea integrating molecular data and ML tools to characterize genetic diversity and trait prediction.
The increasing demand on water due to drought impacts paddy rice production. Therefore, the use of sustainable irrigation methods and smart agricultural practices are of gaining importance. In this study, it was aimed to provide drip irrigation automation by calculating paddy rice evapotranspiration (ETC), irrigation water requirement and irrigation duration based on FAO Penman-Monteith (FAO-PM) model with the developed prototype device via mobile application. Four different irrigation levels (S100, S150, S200 and Farmer (F)) were applied in the study. The research was carried out in the farmer's field located in İpsala district of Edirne province in 2023 and 2024 seasons. The trial was carried out based on daily meteorological parameters in field conditions and the data were stored in a Sheets file on Google Drive. In the study, the seasonal total reference evapotranspiration (ET0) and ETC values were calculated as 571 and 583 mm in 2023 and 655 and 665 mm in 2024. Ranges of seasonal total irrigation water applied were 695-1555 mm in 2023 and 802-1916 mm in 2024. In 2023, the highest yield was obtained in the F application at 6502 kg/ha, while the lowest yield was obtained in the S100 application at 4552 kg/ha. In the second year of the study, the highest yield was obtained in the F application at 7401 kg/ha, while the lowest yield was obtained in the S100 application at 5381 kg/ha. In the first year of the study, irrigation water use efficiencies (IWUE) varied between 0.42-0.65 kg/m3 and between 0.39-0.67 kg/m3 in the second year. The highest IWUE was found in the S100 application in both years. As a result of the study, it was revealed that the system successfully performed irrigation automation. It was seen that the developed device was also applicable for small businesses in terms of ease of use.
.This study evaluated the effects of Lactobacillus plantarum (ATCC 8014) on the postharvest quality of red pepper (Capsicum annuum L. cv. ‘Kapya’). Fruits were treated with suspensions of L. plantarum at 106 CFU mL-1 (LP1), 108 CFU mL-1 (LP2), or sterile water (control), and stored at 8 ± 1 °C and 90-95% relative humidity for 21 days. Physical quality attributes were significantly improved by bacterial treatments. By day 21, weight loss reached 15.6% in the control but was reduced to 10.5% in LP1 and 7.2% in LP2. Firmness decreased to 7.5 N in the control, while LP1 and LP2 maintained higher values of 9.2 N and 11.0 N, respectively. Color was also better preserved in treated fruits, especially under LP2, which maintained higher L*, chroma, and hue values. Chemical analyses confirmed a protective effect of L. plantarum. Ascorbic acid declined from 102 to 60 mg 100 g-1 FW in the control, whereas LP1 and LP2 retained 70 and 76 mg 100 g-1 FW, respectively. Total phenolic content decreased to 68 mg GAE 100 g⁻¹ FW in the control but remained at 76 (LP1) and 83 (LP2). Changes in soluble solids, titratable acidity, and pH were more moderate in treated fruits. The findings demonstrated that L. plantarum, particularly at higher concentrations, effectively maintained the physical, chemical, and microbiological quality of pepper during storage and can serve as a sustainable alternative to chemical preservatives.
