Seeds

Soil salinity poses a major threat to agriculture by severely limiting how well plants grow and produce crops. It strongly inhibits seed germination, a critical stage for plant life. Thus, it is critical to understand the complex ways salinity affects seed germination at the physiological, biochemical, and molecular levels to develop effective salt stress mitigation strategies. This review synthesizes the underlying mechanisms of how salinity inhibits seed germination, the observed impacts of this inhibition, and potential mitigation strategies. The review revealed that high salt concentrations reduce seed germination percentage and increase germination time through multiple mechanisms. They create osmotic stress that reduces water uptake, cause ion toxicity that disrupts critical metabolic activities, and induce oxidative stress. Furthermore, salinity can modify endogenous hormonal profiles, specifically by decreasing germination stimulants like gibberellic acids while increasing inhibitors like abscisic acid. The review finally explored the strategies to mitigate salinity’s adverse effects on seed germination. They include seed priming, a technique involving partial hydration of seeds in an eliciting solution, a promising biotechnological tool to overcome salinity problems during seed germination. Other approaches are the use of organic amendments and the breeding of salt-tolerant varieties. Future research should combine conventional and advanced molecular technologies to develop salt-tolerant cultivars to ensure food security in salt-affected agricultural lands.

Soybeans are widely used in agro-industrial sectors, and global demand for this crop continues to rise. After harvest, however, soybean seeds often lack the appropriate moisture content for storage, making drying a common practice under changing climate conditions. Because temperature is a critical factor during drying, this study aimed to evaluate the effect of air-drying temperature on physiological responses and cytogenetic conformation of soybean seeds. The experiment was conducted under a completely randomized design with four replications for each temperature. Seeds with 23 percent moisture content were dried in a convective dryer equipped with airflow and temperature control at 40 °C, 50 °C, 60 °C, and 70 °C until reaching 13 percent. Samples for physiological and cytological analyses were collected before and after drying. The results indicated that drying temperature influenced seed performance and vigor. Moreover, nuclear alterations were identified as an important component of the genotoxicity caused by high drying temperatures. Overall, air temperatures above 50 °C induced physiological and cytogenotoxic effects, underscoring the need for careful monitoring during seed drying.

Agricultural research has accelerated in recent years, but farmers often lack the time and resources to conduct on-farm experiments, as most of their efforts are devoted to crop production. Seed classification provides essential insights for seed quality control, impurity detection, and yield estimation. Early identification of seed types is critical to reduce costs, minimize risks of poor field emergence, and support efficient crop management. Traditional classification methods rely heavily on manual feature extraction and expert input, which limits scalability and accuracy when dealing with highly similar seed types. To address this challenge, we propose an automated end-to-end deep learning framework for complex multiclass Brassica seed classification. Our framework integrates preprocessing, feature learning, and classification into a unified pipeline, eliminating the need for handcrafted features. Using a newly collected dataset of ten Brassica seed classes characterized by high texture similarity, we develop and evaluate a convolutional neural network optimized through architectural design and hyperparameter tuning. Experimental results demonstrate that the proposed framework achieves a classification accuracy of 93%, outperforming several state-of-the-art pretrained models. These findings highlight the potential of automated end-to-end deep learning models to enhance precision agriculture, providing robust and scalable solutions for seed quality monitoring and agricultural productivity.