Seeds

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.

Sprouts are gaining popularity among consumers worldwide due to their high nutritional properties. A comparative evaluation of novel and environmentally friendly pre-sowing seed treatment techniques was conducted to enhance wheat sprout production. Pulsed electromagnetic field (PEMF), cold atmospheric plasma (CAP), and high-pressure processing (HP) at 200 and 600 MPa were applied on durum wheat seeds for 3 and 10 min. The above techniques, along with ozonation (OZ), were also applied for 3 and 10 min for the “activation” of water that was used for immersion of the wheat seeds. Seed germination percentage, root and shoot length, and seedling dry weight were the measurements for the comparative evaluation of 21 treatments of seeds growing in Petri dishes. The results indicated that CAP, PEMF, and OZ treatments had positive effects on wheat sprout production, while prolonged exposure to HP processing appeared to stress the seeds. Overall, the multiple comparisons of four processing technologies, applied by two methods and at two exposure times, could be a benchmark study for further understanding the response of seeds in pre-sowing techniques.