This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs, low accuracy, and time-consuming traditional tea disease recognition methods. This method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model, effectively alleviating the impact of high similarity between tea diseases on the model’s classification performance. To better solve the problem of few and unevenly distributed tea disease samples, this study introduced a weighted sampling scheme to optimize data processing, which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification data. The experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea (tea algal spot disease, tea white spot disease, tea anthracnose disease, and tea leaf blight disease). After applying the “weighted sampling hierarchical classification learning method” to train 7 different efficient backbone networks, most of their accuracies have improved. The EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21% after adopting this learning method, which is higher than EfficientNet-b2 (98.82%) and MobileNet-V3 (98.43%). In addition, to better apply the results of identifying tea diseases, this study developed a mini-program that operates on WeChat. Users can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple operations. This intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers, consumers, and related scientific researchers and has certain practical value.

Human labor efficiency has become unable to keep the pace with gradually annual citrus increasing production. Highly efficient and intelligent citrus picking and accurate yield estimation is the key to solve the problem. Success heavily depends on detection accuracy, prediction speed, and easy model deployment. Traditional target detection methods often fail to achieve balanced results in all those aspects. An improved YOLOv8 network model with four significant features is proposed. First, a lightweight FasterNet network structure was introduced to the backbone network, which reduced the number of parameters and computations while maintaining high-precision detection. Second, a progressive feature pyramid network AFPN structure was added to the neck network. Third, a parallel multi-branch attention mechanism PMBA was added before the detection head to improve the sensing ability after the feature fusion network. Fourth, a Wise-IoU was introduced to replace the original CIoU loss function to make the whole training process converge faster. Based on this, this study proposes an improved version of the YOLOv8 model: the FAP-YOLOv8. This improved model achieved an average accuracy (mAP@0.5) of 97.2% on the citrus datasets, with an accuracy that was 4.7% higher than the original YOLOv8, which was 19.2%, 7.4%, 5.1%, 4.9%, and 5.2% higher than the other models: Faster R-CNN, CenterNet, YOLOv5s, YOLOx-s, and YOLOv7, respectively. The number of parameters was reduced by 55.45%, the computation was reduced by 20% compared to the YOLOv8 benchmark, and the frame rate reached 46.51 fps to meet the detection requirements of lightweight networks. The experiments showed that the FAP-YOLOv8 models all outperformed the comparison models. Consequently, the proposed FAP-YOLOv8 model can help solve the citrus detection problem in orchards, which can be better applied to edge devices and provides strong support for intelligent orchard management.