MCDCNet: Multi-scale constrained deformable convolution network for apple leaf disease detection

Apple plays a vitally important role in human life and is considered one of the most nutritious fruits. However, the quality and production of the apple industry are seriously restricted by apple leaf diseases and the disease lesions are hard to detect because they often have various scales and deformable geometry. To solve the above problem, this paper proposed a novel Multi-scale Constrained Deformable Convolution Network(MCDCNet), which takes advantage of multi-branch convolution and deformable convolution. Firstly, the novel two-branch convolution network is presented to enhance the discriminatory ability of models for extracting different scales of apple leaf disease. Secondly, different offset intervals are applied to the two kernels of the dual convolution channel separately, which makes the proposed model pay more attention to the deformable geometry features of the lesions and avoid extra weight parameters. Finally, a feature fusion module is constructed to achieve automatic detection of multi-scale apple leaf disease, which combines the output features from the dual convolution channels and performs dimensional operations on the channel dimensions of the feature map. Under the complex natural environment, the accuracy value of the proposed model can reach 66.8%, which is an improvement of 3.85% compared to the existing SOTA models. The experiment results established that MCDCNet has a better feature extraction capability and can efficiently and accurately detect 5 common apple leaf diseases in the natural environment.