The agar extraction process from the seaweed Gelidium corneum generates a substantial quantity of solid residue that is currently underutilized, posing an environmental concern. This study assesses the prospective antifungal efficacy of the residual biomass and explores possible inhibitory pathways against Ascochyta blight of chickpea. Compounds were extracted from the industrial residue using three solvents: water, dichloromethane (DCM), and DCM/ethanol mixture. Assessments included in vitro mycelial inhibition, phytotoxicity on chickpea germination, and in vivo greenhouse trials using foliar and root-drench applications on two varieties. Furthermore, in silico molecular docking was performed to hypothesize potential interactions between identified phenolic constituents and the fungal enzyme lanosterol 14-alpha-demethylase (CYP51). The aqueous extract exhibited the most pronounced antifungal activity, inhibiting 80
Abstract Background Noctuid larvae are major pests in sugar beet fields, with their populations influenced by temperature and humidity. This study aimed to assess their seasonal dynamics and impact on crop damage under field conditions. Results Field trials were conducted from January to June in 2022, 2023, and 2024, with biweekly sampling of 100 randomly selected sugar beet plants. Larval densities and plant damage were recorded, alongside weather data from a local weather station. The beet armyworm, S. exigua (Hübner) was the most abundant noctuid species, with densities increasing from 630 larvae in 2022 to 710 in 2024. Other species included S. littoralis (Boisduval) (440–470 larvae), Spodoptera litura (Fabricius) (290–350 larvae), and Agrotis segetum (Denis & Schiffermüller) (240–280 larvae). Lesser abundant species were S. eridania (Stoll) (120–150 larvae) and A. ipsilon (Rottemberg) (120–140) larvae. Larval densities and plant damage were significantly influenced by sampling week but not by year. In 2022, the highest larval densities and plant damage occurred during weeks 10–14, coinciding with increased rainfall and rising temperatures. Similar patterns were observed in 2023 and 2024, with peak larval populations and notable damage from S. exigua and S. littoralis during weeks 10 and 14, respectively. Temperature and humidity significantly impacted larval densities, with higher populations recorded at moderate temperatures (14–22 °C) and increased humidity (> 60%). A strong correlation was found between larval populations and plant damage (r = 0.9966–0.9977), with peak damage occurring during high larval densities. Conclusions Noctuid larval peaks align with moderate weather. Climate-based timing is key for effective pest control.
The search for natural antioxidants to safeguard against several diseases is expanding rapidly. Interestingly, the levels of antioxidants have been discovered to be greater in the in vitro-raised calli than the plant extracts in vivo. The aim of this research was to standardize the protocols for culturing calli of five potential medicinal herbs and determine their antioxidant and polyphenolic compounds. The calli of carnation, goji berry, harmal, bitter cucumber, and datura were developed from young leaves using Murashige and Skoog media with varied forms and concentrations of cytokinin and auxin in combination after their optimization. Goji berry, carnation, and datura initiated callus in 13 days, faster than bitter cucumber (20 days). Datura had a 28.7
The effects of Rhizophagus irregularis inoculation on the growth, stress tolerance, and acclimatization of argan (Argania spinosa (L.) Skeels) seedlings under in vitro conditions were investigated. The impact of culture medium (minimal (M) vs. modified Strullu-Romand (MSR)) and root type (transformed carrot and chicory roots) on mycorrhization were first assessed. MSR medium slightly outperformed M medium in supporting mycorrhization, with colonization reaching 70
Timely and accurate crop yield estimation is important for sustainable agricultural planning and resource optimization. This study is motivated by the need for a scalable, non-destructive, phenology-aware yield estimation pipeline that can outperform spectral index-based methods. A novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks. The pipeline integrates automated phenological stage mapping using a custom Spatial Phenology Attention and Feature Cross (SPARC) Network, canopy structure modeling, and wheat head segmentation via a U-Net model fine-tuned on masks generated with SAM 2. UAV imagery is collected across 18 timestamps, processed to produce reflectance maps, vegetation indices (VIs), canopy height models (CHMs), and fractional cover maps. Plot-level phenological and morphological features are extracted to train multiple regression models for in-season yield estimation. Results show that combining temporal phenological features with structural head metrics significantly improve estimation accuracy, with Gradient Boosting Regression achieving an R2 of 0.89. The proposed approach not only improves the granularity and timeliness of in-season yield estimations but also enables scalable, non-destructive crop monitoring solutions, providing practical information for both farmers and breeders alike.
