Harmful algal blooms (HABs) are associated with water quality degradation, which damages fisheries, ecosystems, and public health. Consequently, techniques for detecting the occurrence of HABs have been developed globally. However, conventional techniques are unable to discriminate the causative species/genera of HABs objectively. Therefore, the aim of the present study was to develop a technique for frequently analyzing the causative genera of HAB events using repeat digital photography with a stationary device named “HABcam.” The HABcam was installed at Okigamisenishi monitoring station in the inner western area of Ariake Bay, Japan, and the digital images obtained were analyzed to quantify the sea surface color and to estimate the probability of a HAB occurrence on a daily basis using Bayes' theorem. The estimated probability of a HAB occurrence accurately discriminated a HAB occurrence or non-occurrence in the research area on 19/21 days (=90.5%) for Chattonella spp. and 7/8 days (=87.5%) for Skeletonema spp. These findings indicate that this technique can be used to objectively determine the causative genera during HAB events and to observe HABs with high accuracy and at a high frequency.

有害藻华（HABs）与水质下降有关，水质下降会损害渔业，生态系统和公共健康。因此，全球已经开发了用于检测HAB的发生的技术。但是，传统技术无法客观地区分HAB的致病菌种/属。因此，本研究的目的是开发一种技术，该技术通过使用名为“ HABcam”的固定设备进行重复数码摄影来频繁分析HAB事件的病因属。HABcam安装在日本有明湾内西部的Okigamisenishi监测站，并对获得的数字图像进行分析，以量化海面颜色，并使用贝叶斯定理每天评估HAB发生的可能性。查通氏菌属 和7/8天（= 87.5％）的骨骼肌物种。这些发现表明，该技术可用于客观地确定HAB事件期间的病因属，并以高精度和高频率观察HAB。