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Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on field surveys by crop protection experts and experienced blueberry growers. Deep learning models could be a more effective approach, but their performance is highly dependent on the volume and quality of labeled data used for training so that the variance in visual symptoms can be incorporated into a model. However, the available dataset for mummy berry disease detection does not contain enough images collected and labeled from a real-field environment essential for making highly accurate models. Complex visual characteristics of lesions due to overlapping and occlusion of plant parts also pose a big challenge to the accurate estimation of disease severity. This may become a bigger issue when spatial variation is introduced by using sampling images derived from different angles and distances. In this paper, we first present the "cut-and-paste" method for synthetically augmenting the available dataset by generating additional annotated training images. Then, a deep learning-based object recognition model Yolov5s-CA was used, which integrates the Coordinated Attention (CA) module on the Yolov5s backbone to effectively discriminate useful features by capturing channel and location information. Finally, the loss function GIoU_loss was replaced by CIoU_loss to improve the bounding box regression and localization performance of the network model. The original Yolov5s and the improved Yolov5s-CA network models were trained on real, synthetic, and combined mixed datasets. The experimental results not only showed that the performance of Yolov5s-CA network model trained on a mixed dataset outperforms the baseline model trained with only real field images, but also demonstrated that the improved model can solve the practical problem of diseased plant part detection in various spatial scales with possible overlapping and occlusion by an overall precision of 96.30%. Therefore, our model is a useful tool for the estimation of mummy berry disease severity in a real field environment. [ABSTRACT FROM AUTHOR]

As global temperatures rise, a growing need exists for understanding the impacts of warming on crop production. Warming not only changes crop physiology and growth but also the weeds, insect pests, and diseases of crops including wild lowbush blueberries, which have not been studied till now. Changes in pest pressures can cause instability in production and will require changes in management practices and the development of mitigation strategies. The objective of this study was to determine the impacts of warming on the prevalence of major weeds, insect pests, and diseases of the wild blueberry production system. We selected six genotypes of wild lowbush blueberries in a commercially managed wild blueberry field in Maine Northeast USA and used open-top-chambers (OTCs) to study the effects of warming for two years (2019 and 2020). Both active-heating OTCs (elevated monthly mean temperatures by 3.3 °C) and passive-heating OTCs (elevated by 1.2 °C) were employed and compared with ambient controls. Our results showed that warming did not change the prevalence of red leaf disease, blueberry gall midge, red-striped fireworm, or any weed species. In contrast, the incidence of Sphaerulina leaf spot, powdery mildew, and other leaf spot disease were significantly lower under warming treatments compared to the ambient control at the end of the growing season in 2020. Overall, different pests responded to warming differently, inviting further research to reveal the mechanisms. The lower overall pressure of leaf spot disease under warming was probably due to decreased air humidity. [ABSTRACT FROM AUTHOR]