深度学习（DL）算法在不同领域显示出令人印象深刻的性能。其中，由于一些有趣的模式，在过去的几十年中，音频吸引了许多研究人员 - 尤其是在音频数据的分类中。为了更好地执行音频分类，功能选择和组合起着关键作用，因为它们有可能制造或破坏任何DL模型的性能。为了调查这一角色，我们对具有各种最先进的音频特征的多种尖端DL模型（即卷积神经网络，Extricnet，Mobilenet，Supper Vector Machine和Multi-Pecceptron）的性能进行了广泛的评估。 （即MEL频谱图，MEL频率Cepstral系数和零交叉率）在三个不同的数据集上独立或作为组合（即通过结合）（即免费的口语数据集，音频urdu数据集和Audio Gujarati Digits Digaset数据集） ）。总体而言，结果建议特征选择取决于数据集和模型。但是，特征组合应仅限于单独使用时已经实现良好性能的唯一特征（即主要是MEL频谱图，MEL频率Cepstral系数）。这种功能组合/结合使我们能够胜过以前的最新结果，而与我们选择的DL模型无关。

Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Insect pollination monitoring and management are therefore essential for improving crop production and food security. Computer vision facilitated pollinator monitoring can intensify data collection over what is feasible using manual approaches. The new data it generates may provide a detailed understanding of insect distributions and facilitate fine-grained analysis sufficient to predict their pollination efficacy and underpin precision pollination. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage and often constrained to a single insect species. This limits its relevance to agriculture. Therefore, in this article we introduce a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction across large agricultural areas. Our system is comprised of edge computing multi-point video recording, offline automated multispecies insect counting, tracking and behavioural analysis. We implement and test our system on a commercial berry farm to demonstrate its capabilities. Our system successfully tracked four insect varieties, at nine monitoring stations within polytunnels, obtaining an F-score above 0.8 for each variety. The system enabled calculation of key metrics to assess the relative pollination impact of each insect variety. With this technological advancement, detailed, ongoing data collection for precision pollination becomes achievable. This is important to inform growers and apiarists managing crop pollination, as it allows data-driven decisions to be made to improve food production and food security.