Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.

During pineapple cultivation, detecting and counting the number of pineapple flowers in real time and estimating the yield are essential. Deep learning methods are more efficient in real-time performance than traditional manual detection. However, existing deep learning models are characterized by low detection speeds and cannot be applied in real time on mobile devices. This paper presents a lightweight model in which filter pruning compresses the YOLOv5 network. An adaptive batch normalization layer evaluation mechanism is introduced to the pruning process to evaluate the performance of the subnetwork. With this approach, the network with the best performance can be found quickly after pruning. Then, an efficient channel attention mechanism is added for the pruned network to constitute a new YOLOv5_E network. Our findings demonstrate that the proposed YOLOv5_E network attains an accuracy of 71.7% with a mere 1.7 M parameters, a model size of 3.8 MB, and an impressive running speed of 178 frames per second. Compared to the original YOLOv5, YOLOv5_E shows a 0.9% marginal decrease in accuracy; while, the number of parameters and the model size are reduced by 75.8% and 73.8%, respectively. Moreover, the running speed of YOLOv5_E is nearly twice that of the original. Among the ten networks evaluated, YOLOv5_E boasts the fastest detection speed and ranks second in detection accuracy. Furthermore, YOLOv5_E can be integrated with StrongSORT for real-time detection and counting on mobile devices. We validated this on the NVIDIA Jetson Xavier NX development board, where it achieved an average detection speed of 24 frames per second. The proposed YOLOv5_E network can be effectively used on agricultural equipment such as unmanned aerial vehicles, providing technical support for the detection and counting of crops on mobile devices.

Introduction: Preserving the marine ecological environment and safeguarding marine species is a global priority. However, human overfishing has led to a drastic decline in fish species with longer growth cycles, disrupting the equilibrium of the marine ecosystem. To address this issue, researchers are turning to deep learning techniques and state-of-the-art underwater devices, such as underwater robots, to explore the aquatic environment and monitor the activities of endangered populations. This approach has emerged as a focal point of recent research in protecting the marine ecological environment. This study employs a deep learning-based object detection algorithm to identify fish species in complex underwater environments.Methods: The algorithm is built upon the You Only Look Once version 7(YOLOv7) algorithm, with the addition of the attention mechanism Convolutional Block Attention Module (CBAM) in the network’s backbone. CBAM enhances the feature maps through the fusion of spatial attention and channel attention, ultimately improving the robustness and accuracy of the model’s inference by replacing the original loss function CIoU with SCYLLAIntersection over Union(SIoU). In this paper, the rockfish pictures in the dataset Label Fishes in the Wild published by the National Marine Fisheries Service are selected, and the underwater image enhancement model (UWCNN) is introduced to process the pictures.Result: The experimental results show that the mean average precision (mAP) value of the improved model on the test set is 94.4%, which is 3.5% higher than the original YOLOv7 model, and the precision and recall rate are 99.1% and 99%, respectively. The detection performance of the algorithm in the field of complex underwater environment is improved.Discussion: The underwater fish detection scheme proposed in this study holds significant practical value and significance in promoting the conservation of marine ecosystems and the protection of fish species.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive and error-prone. In this study, a novel method, dubbed the “SSM-Method”, based on convolutional neural network and traditional image processing technology has been developed for the efficient and precise measurement of rice spikelet size parameters on rice panicle structures. Firstly, primary branch images of rice panicles were collected at the same height to build an image database. The spikelet detection model using convolutional neural network was then established for spikelet recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. The performance of the developed SSM-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of spikelet length for two rice varieties (Huahang15 and Qingyang) were 0.26 mm and 0.30 mm, respectively, while the corresponding RMSE of spikelet width was 0.27 mm and 0.31 mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.