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...
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...
Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalabilit...
Tropical rice systems exhibit high annual rates of heavy metal accumulation, requiring accurate identification of accumulation drivers in rice-growing ecosystems to ensure regional food security. Therefore, we collected 229 paired soil and rice sampl...
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...
The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-i...
BACKGROUND: Plant growth regulators (PGRs) have proven effective in alleviating salt stress damage in crops, yet the predictive hierarchy of physiological traits governing biomass under combined salt-PGR regimes remains unclear. In this study, we app...
Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surveys to examine their abundance and composition in paddy fields involve human visual inspection, which requires considerable time and effort by expert ...
Accurate detection of rice pests helps farmers take timely control measures. This study compares different attention mechanisms for rice pest detection in complex backgrounds and demonstrates that a human vision-inspired Bionic Attention (BA) mechani...
Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) mode...
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