Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different ty...
Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and compr...
The global industry of tobacco (Nicotiana tabacum L.) is a profitable one comprising various products, including cigars, cigarettes, chewing tobacco, and smokeless tobacco. The internal quality of the cigarettes is highly related to the chemical comp...
Accurate frost detection on leaf surfaces is critical for agricultural monitoring, yet existing methods struggle with segmentation errors caused by complex backgrounds (blurred, soil, weeds) and subtle frost-leaf texture differences. To address this,...
Journal of agricultural and food chemistry
Oct 1, 2025
Traditional pesticide formulations suffer from poor adhesion and limited deposition efficiency, causing overuse and ecological risks. Functional nanocarriers present a promising solution by leveraging bioinspired adhesive and structural topographies ...
Infrared thermal imaging offers a rapid and sensitive approach to assessing temperature changes in plants caused by salt stress, even in the early stages of exposure. Given the increasing prevalence of salt contamination in the environment, it is ess...
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditio...
Convolutional Neural Networks (CNNs) stand as indispensable tools in deep learning, capable of autonomously extracting crucial features from diverse data types. However, the intricacies of CNN architectures can present challenges such as overfitting ...
Plant leaf diseases significantly impact crop yield and quality, causing substantial economic loss and risking food security. Despite significant progress in the field of automated plant disease diagnosis, there are still several challenges that need...
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on...
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