Deep learning models need sufficient training samples to support them in the training process; otherwise, overfitting occurs, resulting in model failure. However, in the field of smart agriculture, there are common problems, such as difficulty in obtaining high-quality disease samples and high cost. To solve this problem, this paper proposed a high-quality image augmentation (HQIA) method for generating high-quality rice leaf disease images based on a dual generative adversarial network (GAN). First, the original samples were used to train Improved Training of Wasserstein GANs (WGAN-GP) to generate pseudo-data samples. The pseudo-data samples were put into the Optimized-Real-ESRGAN (Opt-Real-ESRGAN) to generate high-quality pseudo-data samples. Finally, the high-quality pseudo-data samples were put into the disease classification convolutional neural network, and the effectiveness of the method was verified by indicators. Experimental results showed that this method can generate high-quality rice leaf disease images, and the recognition accuracy of high-quality rice disease image samples augmented by this method was 4.57% higher than that of using only the original training set on ResNet18 and 4.1% higher on VGG11. Compared with the data augmentation method only by WGAN-GP, the accuracy of ResNet18 increased by 3.08%, and the accuracy of VGG11 increased by 3.55%. The results demonstrate the effectiveness of the proposed method with limited training datasets.

In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.

Food quality detection is an important method for ensuring food safety. Efficient quality detection methods can improve the efficiency of food circulation and reduce storage and labor costs. Traditional methods use instrumentation, testing reagents, or manual labor. These methods take a long time to detect, are time-consuming and labor-intensive, and require professionals to operate. Fruit, as a high-value food that provides essential nutrition for human beings, is susceptible to spoilage during packaging, transportation, and sales, so the freshness and safety assurance of fruit are a hot and difficult area of current research. Therefore, for the detection of fruit freshness, this paper proposes an efficient and nondestructive way to detect fruit freshness by using the machine learning algorithm convolutional neural network (CNN). This paper shows that convolutional neural networks have good performance in identifying the freshness of fruits through extensive experimental results and discusses the overfitting of machine learning based on the experimental results.

Deep learning models are widely used in crop leaf disease image recognition. These models can be divided into two categories: global model and local model. The global model directly takes the whole leaf disease images as input to training and recognition. It can achieve end-to-end training and recognition, which is very convenient to use. But this kind of model cannot very accurately and completely extract the features from the very small diseased spots in the image. Before training and recognizing, the local model needs to extract the diseased spots part from the image by image segmentation technology. Then the local model takes the disease spots part images as input to training and recognition. Features extracted by local model are more accurate and complete. But this kind of model cannot achieve end-to-end training and recognition, and the image segmentation will bring additional overhead. Considering the disadvantage of global model and local model, we proposed a crop leaf disease image recognition method based on bilinear residual networks (named DIR-BiRN). DIR-BiRN extracts features by two residual networks feature extractors and then integrates the features by a bilinear pooling function. By this way, it can extract features more accurately and completely while achieving end-to-end training and recognition. Experiments on the PlantVillage dataset show that, when compared with the standard ResNet-18 model, the DIR-BiRN improves on accuracy performance, recall performance, precision performance, and F1-measure performance by averages of 0.2918, 0.81641, 0.59185, and 0.52151 percentage points, respectively.

Deep learning has revolutionized numerous fields, notably image classification. However, conventional methods in agricultural pest recognition struggle with the long-tail distribution of pest image data, characterized by limited samples in rare pest categories, thereby impeding overall model performance. This study proposes two state-of-the-art techniques: Instance-based Data Augmentation (IDA) and Constraint-based Feature Tuning (CFT). IDA collaboratively applies resampling and mixup methods to notably enhance feature extraction for rare class images. This approach addresses the long-tail distribution challenge through resampling, ensuring adequate representation for scarce categories. Additionally, by introducing data augmentation, we further refined the recognition of tail-end categories without compromising performance on common samples. CFT, a refinement built upon pre-trained models using IDA, facilitated the precise classification of image features through fine-tuning. Our experimental findings validate that our proposed method outperformed previous approaches on the CIFAR-10-LT, CIFAR-100-LT, and IP102 datasets, demonstrating its effectiveness. Using IDA and CFT to optimize the ViT model, we observed significant improvements over the baseline, with accuracy rates reaching 98.21%, 88.62%, and 64.26%, representing increases of 0.74%, 3.55%, and 5.73% respectively. Our evaluation of the CIFAR-10-LT and CIFAR-100-LT datasets also demonstrated state-of-the-art performance.

Real-time strawberry volume estimation is a pivotal technology for automated harvesting and precision grading. However, conventional contact methods are prone to damaging fruits, while existing vision-based approaches struggle to balance high accuracy with low computational overhead. To address these challenges, this study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework. First, to precisely extract target contours in complex backgrounds, we designed Deformable Feature Aware-YOLO (DFA-YOLO) based on the YOLO11-seg architecture. This model enhances the geometric perception of irregular fruit edges and effectively overcomes the challenges of background noise and multi-scale variations, providing high-precision masks for subsequent spatial mapping. Subsequently, a principal-axis-slicing algorithm extracts the mask’s centroid and principal axis, perpendicularly slicing the mask into infinitesimal micro-slices. By computing and accumulating the pixel-space volume of these slices, the system converts them into precise 3D physical volumes based on RGB-D depth mapping. The entire system was deployed on an NVIDIA Jetson Orin edge computing platform and validated in a greenhouse. Experimental results demonstrate that the estimated volume highly agrees with the true volume, achieving a coefficient of determination (R²) of 0.945 and a mean absolute percentage error (MAPE) of 9.0%. Under typical operating conditions (1–5 targets per field of view), the system maintains an overall frame rate of 8–15 FPS, requiring only 55 ms for single-fruit estimation. This method exhibits favorable stability and lightweight efficiency under the tested greenhouse conditions, offering a reliable solution for real-time non-destructive crop phenotypic monitoring in computationally constrained agricultural environments.