Accurate and rapid detection of mango fruits and peduncles can often be required in natural environments. However, the current detection is limited to similar background and target colors, foliage occlusion, as well as overlapping fruits. In this study, an improved model, MAL-YOLOv10n, was proposed to detect the mango fruits and peduncles using the YOLOv10n framework. Specifically, the detection accuracy of small target peduncles was optimized to tackle the challenges. Several modifications were made to the architecture and key modules, resulting in significant improvements in the model performance. Firstly, the original C2f module was reengineered in the backbone network in order to create an RFAM-C2f module. In the traditional C2f module, the Bottleneck structure relied mainly on standard 3×3 convolutions for feature extraction. The current approach often failed to capture the global contextual information in the complex scenes with similar background hues, occlusion, or overlapping fruits. To overcome this limitation, the 3×3 convolution in the Bottleneck was replaced by the receptive-field attention convolution (RFAConv). An attention mechanism, RFAConv, was integrated to expand the receptive field. Global contextual cues were captured to extract the initial feature at a more robust stage. Additionally, a Convolutional Block Attention Module (CBAM) was added after the modified Bottleneck to further refine feature selection. The CBAM was sequentially applied to the channel and spatial attention in order to automatically focus on the target regions and effectively suppress background noise and interference. The RFAM-C2f module was used to accurately extract the effective features of the mango fruits and peduncles. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced into the feature pyramid network in order to improve the detection accuracy of small target peduncles. Conventional networks of unidirectional feature pyramids often suffered from insufficient information flow during multi-scale feature fusion. A bidirectional transmission, along with learnable weighted coefficients, was employed to adaptively integrate the features at the different scales. The low-level information was well fused with the high-level semantic information in order to effectively mitigate the loss of feature information due to the small size of the targets. Experimental results demonstrated that the incorporation of BiFPN significantly improved both recall and precision for the small peduncle detection. Finally, the neck network was optimized to introduce the partial convolution (PConv). A lightweight module, PConv-C2f, was formed for the localized partial convolution, thereby reducing unnecessary computations and memory accesses while maintaining effective feature extraction. A robust extraction of some feature was obtained to decrease the computational complexity and parameter count. The MAL-YOLOv10n significantly outperformed the original YOLOv10n model at various metrics. Specifically, the improved model achieved a precision of 94.9%, a recall of 89.7%, and a mean average precision (mAP) of 95.5%, indicating the improvements of 3.1, 3.3, and 2.5 percentage points over YOLOv10n, respectively. The detection was achieved at a frame rate of 119.6 frames per second. In light weighting, MAL-YOLOv10n reduced the floating-point operations, parameter count, and model size by 12%, 3.7%, and 8.6%, respectively. Furthermore, the superior performance of the MAL-YOLOv10n was achieved in complex scenarios and small target detection, compared with the mainstream object models, including Faster-RCNN, SSD, YOLOv5s, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv10n, YOLOv10s, YOLO11s, YOLOv12n, and RT-DETR. In summary, an optimal balance between detection speed and accuracy was obtained for the exceptional robustness under complex environmental conditions. The finding can also provide valuable technical support for mango harvesting in challenging natural scenes.

Here, the ZAL-YOLOv8n model was proposed to rapidly and accurately detect the apple appearance quality in natural scenes using the improved YOLOv8n model. The baseline model was selected as the YOLOv8n model, the lightest model in the YOLOv8 series. Subsequently, the improved model was deployed on the mobile edge devices, despite its relatively lower accuracy of recognition. The apple appearance quality was classified into three categories: Immature, Mature, and Fall ill, according to the influencing factors, such as the fruit maturity and health. When the fruit with the greenish-blue peel was marked as “Immature”. Once the peel was red or a mix of red and yellow, the fruit was marked as “Mature”. If there were disease spots on the peel, the fruit was marked as “Fall ill”. Two common apple diseases, namely “Ring rot” and “Scab”, were further identified from the disease category. Firstly, the partial convolution (PConv) and the efficient multi-scale attention mechanism (EMA) were integrated as the EP-C2f module, in order to replace the C2f module in the backbone network. The EMA attention mechanism was employed to redistribute the attention weights, and then group all channels with the same number of channels. Effective information on all channels was retained to realize the interaction between channel and spatial position. Thereby the target area was focused on enhancing the feature extraction for apples under complex occlusion. Simultaneously, the dynamic convolution of the PConv was used to filter out the effective feature data. The model size and parameters were reduced after extraction. Secondly, the MPDIoU was introduced as the boundary regression loss function, in order to realize the accurate positioning of apple skin lesion areas in diseased apples. The reason was that the original boundary box regression loss function (CIoU) of the YOLOv8 model failed to realize the rapid fitting, where the types and degrees of pathogen infections varied among diseased apples. The new position fitting was accelerated to distinguish and locate the lesion edges, in order to minimize the distance between the upper left and lower right corners of the predicted and the real box. Finally, the Slim-neck architecture was utilized to reconstruct the feature fusion network of the YOLOv8 model. The lightweight of the neck network was achieved to enhance the operating speed. The experimental results indicate that the accuracy, recall, and mean average precision of the ZAL-YOLOv8n model increased by 3.4, 1.1, and 1.3 percentage points, respectively. Meanwhile, the floating-point operations, parameter quantity, and model size were reduced by 22.2%, 17.7%, and 15.9%, respectively, fully meeting the deployment requirements for the mobile edge devices. The ZAL-YOLOv8n model was performed on the highly precise detection and quality identification of the apples with different appearance qualities in natural scenes. The high degree of lightweight was also achieved to balance between the accuracy and speed, in order to realize the real-time detection on the low-computing-power devices. Therefore, the improved model can also provide technical support to the research and development of intelligent robots for apple picking.

Green lawns can play a crucial role in the landscape and ecological environments with the rapid urbanization in China. However, there is an ever-increasing demand for the daily maintenance of lawns. Among them, the heterogeneous weeds can often compete with the native grass for nutrients and growing space in lawns, leading to the low overall quality of urban greenery. The aesthetic appeal of the lawn can also be diminished after premature aging. Thereby, the accurate and rapid detection of the weed can be required for deep learning and computer vision. This study aims to improve the efficiency and accuracy of the detection of heterogeneous weeds in natural environments. A lightweight algorithm was also proposed using the original YOLOv8n model. Firstly, the deformable convolutional network v2 (DCNv2) was employed to combine with the c2f convolutional layers in the backbone network. The offsets were then introduced to enhance the feature extraction of the improved model. Different regular shapes of the heterogeneous weeds were captured after optimization. Additionally, a modulation mechanism was incorporated to control the contribution of each sampling point to the output. The precision and robustness were then improved to focus more on the target regions, thereby reducing the interference from background noise. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced in the neck network. Multi-scale features were efficiently fused using bidirectional cross-scale connections and weighted feature fusion. The targets were detected at varying scales. Furthermore, the BiFPN significantly reduced the computational overhead, compared with the traditional feature pyramid network (FPN). The redundant connections were also eliminated to incorporate the lightweight weighting mechanisms. The efficiency and generalization of feature fusion were also improved after feature fusion. Lastly, the traditional intersection over union (IoU) loss function was replaced with the inner-IoU (inner intersection over union) loss function. The objective function of the bounding box regression was then optimized to learn the target location information, thereby improving the convergence speed and detection performance. More precise guidance was realized for the improved model. The experimental results show that the improved YOLOv8-LDB model reduced the number of parameters, the computational cost, and the model size by 32.7%, 13.6%, and 31.8%, respectively, compared with the original YOLOv8n. While the mean average precision (mAP) increased by 3.2 percentage points. The performance of the improved YOLOv8-LDB model was better than that of the seven commonly used network models (including Faster-RCNN, SSD, YOLOv5s, YOLOv5n, YOLOv7n, YOLOv10n, and YOLOv11n), in terms of the precision, parameter count, computational cost, and model size. The mean average precision was improved by 21.6, 9.6, 1.6, 7.7, 2.4, 4.1, and 3.3 percentage points, respectively. Additionally, the detection speed increased from 80.4 to 87.1 frames per second. The inference efficiency was also enhanced for the real-time detection of heterogeneous weeds in natural environments. The YOLOv8-LDB algorithm demonstrated superior performance across multiple metrics. Automatic sprayers and weeding robots can be integrated to realize variable-rate precision spraying and targeted weed control. The automation level of lawn management can also be further advanced in the future. The lightweight improved model can be expected to find wide applications in smart city greening and precision agriculture. The findings can also provide technical support to protect the sustainable ecological environment in urban areas.

In order to solve the problems of complex detection scene, low detection accuracy and high computational complexity of tomato leaf pests and diseases in natural environment, a deep learning algorithm SLP-YOLOv7-tiny was proposed. Firstly, part of the 3×3 convolution Conv2D Convolution in the backbone feature extraction network is changed to distributed migration convolution DSConv2D (2D Depthwise Separable Convolution) to reduce the computing load of the network and speed it up. Less memory usage; Secondly, the parameter-free attention module (SimAM) was integrated into the backbone feature extraction network to enhance the model's ability to effectively extract and integrate features of pests and diseases. Finally, the original YOLOv7-tiny CIOU loss function is replaced with Focal EIOU loss function to accelerate the model convergence and reduce the loss value. The test results show that The overall identification accuracy, recall rate, average accuracy mAP_0.5 (the average accuracy when the IOU threshold is 0.5), and mAP_{0.5 ~ 0.95} (the average accuracy when the IOU threshold between 0.5 and 0.95) of SLP-YOLOv7-tiny model are 95.9%, 94.6%, and 98%, respectively Compared with YOLOv7-tiny before the improvement, they respectively increase by 14.7, 29.2, 20.2 and 30 percentage points. Meanwhile, the calculation amount decreases by 62.6% and the detection speed increases by 13.2%. Compared with YOLOv5n, YOLOv5s, YOLOv5m, YOLOv7, Yolov7-Tiny, Faster-RCNN and SSD target detection models, mAP_0.5 improved by 2.0, 1.6, 2.0, 2.2, 20.2, 6.1 and 5.3 percentage points respectively. The computing capacity was only 31.5%, 10.6%, 4.9%, 4.3% and 3.8% of YOLOv5s, YOLOv5m, YOLOv7, father-RCNN and SSD. The results show that SLP-YOLOv7-tiny can accurately and quickly detect tomato leaf diseases and pests, and the model is small, which can provide certain technical support for the development of rapid and accurate detection of tomato leaf diseases and pests.SLP-YOLOv7-tiny can be expected to accurately and rapidly detect the tomato leaf diseases and insect pests. The small model was more conducive to the migration application. A comparison was also performed on the detection performance of different attention mechanisms. The SimAM attention mechanism shared the better screening of effective feature information on the YOLOv7-tiny model. The disease spots were identified to effectively improve the accuracy of the model, compared with the SENet and CBAM attention mechanism. The ablation test was carried out to verified the added module. The high detection accuracy and computational efficiency were suitable for the deployment in the natural environment with the limited computing resources, such as mobile terminals. The visualization of model detection show that the SLP-YOLOv7-tiny model can be used to learn and distinguish the fine features of tomato leaf disease spots. The detection accuracy was also better than that of the YOLOv7-tiny model. Moreover, the SLP-YOLOv7-tiny model can be used to accurately identify the diseases and pests of diseased tomato leaves in the natural shooting environment. The high detection accuracy can be applied for the multiple diseases and pests. The better identification was also obtained for the small features in the similar diseases and pests. The finding can provide the technical support for the rapid and accurate detection of tomato leaf diseases and insect pests.