A binarizationbased Faster R-CNN （Binary Faster R-CNN） region detection neural network model is proposed for the study of citrus leaf disease detection and recognition techniques in natural scenarios. The improved model transforms the original Faster R-CNN fully connected layer neural network into a binary, fully convolutional neural network. The experimental results showed that the average recognition rate of the model was 87.2%, 87.6%, 89.8%, 86.4%, and 86.6% for black spot, ulcer, citrus_greening, scab, and healthy leaves of citrus, respectively, and the overall average recognition rate of the model was 87.5%. The recognition speed of the model was improved by 0.53 s compared with the Faster R-CNN network, and the detection time up to 0.31 s per image. The model size was reduced to 153 MB, and the floatingpoint computational power was 2.58×109, while the model converged quickly, ensuring the effectiveness of detection and robustness of the model. The method has good recognition speed and robustness for citrus leaf disease detection in complex natural environments and is of great research significance for citrus disease prevention.

In order to study the image recognition technology of citrus in natural environment and realize the early yield prediction of citrus, an improved D-YOLOV3 algorithm was proposed. In this study, a green citrus image data set was constructed. In order to enhance the diversity of the data set, preprocessing operations were carried out on the collected images, including color balance, brightness transformation, rotation transformation, blur, and noise. To solve the problem that gradient information in deep networks will disappear or over-expand with the deepening of the network, the improved model adopts DenseNet's dense connection mechanism to replace the last three lower sampling layers of feature extraction network Darknet53 in the YOLOV3 network to enhance the propagation of features and realize feature reuse. The D-YOLOV3 model was tested by the self-made data set, and experiments were conducted on the recognition performance of the network before and after the modification, different pretreatment methods, different amounts of fruit, and different amounts of data images on the model. The experimental results show that compared with the traditional YOLOV3 model, the accuracy rate of the improved D-YOLOV3 model is increased by 6.57%, the recall rate is increased by 2.75%, the F ₁ score is increased by 4.41%, the intersection ratio is increased by 6.13%, and the average single test time is 0.28 s. Different preprocessing methods enhance the robustness of the model, among which the fuzzy processing has the greatest influence on the performance of the model and the rotation change has the least influence. In the multi-fruit scene, the improved model has a higher recognition accuracy of 5.53% than before, which proves that the model has advantages in recognizing multi-target fruit in the actual scene. Only 1,250 images are needed to fit the model. The research results show that the D-YOLOV3 model proposed in this paper has high accuracy in recognizing immature green citrus in the natural environment, providing technical support for the early production measurement of citrus.