HLNet Model and Application in Crop Leaf Diseases Identification

Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture

Real-Time Detection of Crops with Dense Planting Using Deep Learning at Seedling Stage

Crop seedlings are similar in appearance to weeds, making crop detection extremely difficult. To solve the problem of detecting crop seedlings in complex field environments, a seedling dataset with four crops was constructed in this study. The single leaf labeling method was proposed as an alternative to conventional labeling approaches to improve the detection accuracy for dense planting crops. Second, a seedling detection network based on YOLOv5 and a transformer mechanism was proposed, and the effects of three features (query, key and value) in the transformer mechanism on the detection accuracy were explored in detail. Finally, the seedling detection network was optimized into a lightweight network. The experimental results show that application of the single leaf labeling method could improve the mAP0.5 of the model by 1.2% and effectively solve the problem of missed detection. By adding the transformer mechanism module, the mAP0.5 was improved by 1.5%, enhancing the detection capability of the model for dense and obscured targets. In the end, this study found that query features had the least impact on the transformer mechanism, and the optimized model improved the computation speed by 23 ms·frame−1 on the intelligent computing platform Jetson TX2, providing a theoretical basis and technical support for real-time seedling management

HLNet Model and Application in Crop Leaf Diseases Identification

Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture

Enhanced photorefractive properties of indium co-doped LiNbO3:Mo crystals

We grew a set of indium and molybdenum co-doped lithium niobate crystals with various indium doping concentrations and investigated their photorefractive properties at different wavelengths (442, 488 and 532 nm). It was found that the diffraction efficiency of 1.0 mol% indium and 0.5 mol% molybdenum co-doped lithium niobate crystal could reach 61.57% at 488 nm. Moreover for 3.0 mol% indium and 0.5 mol% molybdenum co-doped lithium niobate crystal, the response time was greatly shortened to 0.61, 0.76, and 0.74 s at 442, 488, and 532 nm, respectively, while the photorefractive sensitivity reached as high as 7.35 cm/J at 442 nm. These results indicate that co-doping of indium is an efficient way to further enhance the photorefractive properties of molybdenum-doped lithium niobate crystal

Photorefractive Properties of Molybdenum and Hafnium Co-Doped LiNbO3 Crystals

A series of LiNbO3: Mo, Hf crystals with 0.5 mol % fixed MoO3 and various HfO2 concentrations (0.0, 2.0, and 3.5 mol %) were grown by the Czochralski technique. The photorefractive properties of the LiNbO3: Mo, Hf crystals were investigated by two-wave coupling measurements and the beam distortion method was employed to obtain the optical damage resistance ability. The UV-visible and OH− absorption spectra were also studied. The experimental results imply that the photorefractive properties of LiNbO3: Mo crystals at laser wavelengths of 532, 488, and 442 nm can be greatly enhanced by doping HfO2 over the threshold concentration. At 442 nm especially, the response time of LN: Mo, Hf3.5 can be shortened to 0.9 s with a diffraction efficiency of 46.07% and a photorefractive sensitivity reaching 6.28 cm/J. Besides this, the optical damage resistance at 532 nm is 3 orders of magnitude higher than that of the mono-doped LiNbO3: Mo crystal, which is beneficial for applying it in the field of high-intensity lasers

Add module comparison results.

Add module comparison results.</p

Comparison results of different network models: A is training accuracy of model, B is validation accuracy of model.

Comparison results of different network models: A is training accuracy of model, B is validation accuracy of model.</p

The CBAM structure used in the model.

The CBAM structure used in the model.</p

Comparison of model performance results with other researchers.

Comparison of model performance results with other researchers.</p