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Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem of data deficiency in predicting productivity of agroecosystems, where productivity is a nonlinear function of various interacting biotic and abiotic factors. We describe the process of building metamodels (machine learning models built and trained on simulation data) from simulations built for one agroecosystem (US wild blueberry) as the source domain, where the data resource is abundant. Metamodels are evaluated and the best metamodel representing the system dynamics is selected. The best metamodel is re-parameterized and calibrated to another agroecosystem (Northeast China bog blueberry) as the target domain where field collected data are lacking. Experimental results showed that our metamodel developed for wild blueberry achieved 78% accuracy in fruit-set prediction for bog blueberry. To demonstrate its usefulness, we applied this calibrated metamodel to investigate the response of bog blueberry to various weather conditions. We found that an 8% reduction in fruit-set of bog blueberry is likely to happen if weather becomes warmer and wetter as predicted by climate models. In addition, southern and eastern production regions will suffer more severe fruit-set decline than the other growing regions. Predictions also suggest that increasing commercially available honeybee densities to 18 bees/m2/min, or bumble bee densities to 0.6 bees/m2/min, is a viable way to compensate for the predicted 8% climate induced fruit-set decline in the future.

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.

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