RESULTS: Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. Using a generalised linear model (GLM) and a Boruta feature selection algorithm, we also showed that the colour and architecture of the sticky traps significantly influenced the performance of the model.
CONCLUSION: Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
RESULTS: It was found that colour change was significantly reduced at elevated heat (100 °C, *∆E = 0.81 ± 0.05), reduced pH (pH 3, *∆E = 0.59 ± 0.04) and length of light exposure (*∆E = 3.16 ± 0.04). Antioxidant activity decreased under all treatments. Among the temperatures tested, fucoxanthin exhibited the highest activity at 60 °C, ranging from 0.92 to 3.04 mg Trolox equivalents (TE) g-1. Significant activity reductions (P