Recently, numerous multiobjective evolutionary algorithms (MOEAs) have been proposed to solve the multiobjective optimization problems (MOPs). One of the most widely studied MOEAs is that based on decomposition (MOEA/D), which decomposes an MOP into a series of scalar optimization subproblems, via a set of uniformly distributed weight vectors. MOEA/D shows excellent performance on most mild MOPs, but may face difficulties on ill MOPs, with complex Pareto fronts, which are pointed, long tailed, disconnected, or degenerate. That is because the weight vectors used in decomposition are all preset and invariant. To overcome it, a new MOEA based on hierarchical decomposition (MOEA/HD) is proposed in this paper. In MOEA/HD, subproblems are layered into different hierarchies, and the search directions of lower-hierarchy subproblems are adaptively adjusted, according to the higher-hierarchy search results. In the experiments, MOEA/HD is compared with four state-of-the-art MOEAs, in terms of two widely used performance metrics. According to the empirical results, MOEA/HD shows promising performance on all the test problems.

Deep learning is a hot research topic in image processing and computer vision. It has been employed in many fields owing to the excellent performance. However, so far, deep learning methods have been rarely applied in the task of plant disease recognition, except that some existing work focuses on it from a public dataset of images zoomed on plant leaves. The main reasons behind the limited usage of deep learning models in plant disease recognition include: the large volume of deep learning models, which is difficult to deploy on embedded systems, the great computational complexity, which requires large memory overhead, the complex backgrounds of experimental materials, which are hard to train an efficient model, and others. To address these challenges, the paper proposes a valid lightweight network architecture, namely MobInc-Net, to perform the crop disease recognition and detection. In this study, the Inception module was enhanced by replacing the original convolutions with depth-wise and point-wise convolutions, and then the modified Inception (M-Inception) module paired with the pre-trained MobileNet was chosen as the backbone extractor to extract high-quality image features. After that, the completely linked Softmax layer with the actual number of categories and the SSD block were separately added behind the foundation network for classifying and detecting crop disease types. To train an efficient model, the two-stage transfer learning was applied in the model training. Experimental findings show that the proposed method can attain the desired performance with an average recognition accuracy of 99.21% on the public dataset and 97.89% on the local dataset.

Crop diseases have a devastating effect on agricultural production, and serious diseases can lead to harvest failure entirely. Recent developments in deep learning have greatly improved the accuracy of image identification. In this study, we investigated the transfer learning of deep convolutional neural networks and modified the network structure to improve the learning capability of plant lesion characteristics. The MobileNet with squeeze‐and‐excitation (SE) block was selected in our approach. Integrating the merits of both, the pre‐trained MobileNet and SE block were fused to form a new network, which we termed the SE‐MobileNet, and was used to identify the plant diseases. In particular, the transfer learning was performed twice to obtain the optimum model. The first phase trained the model for the extended layers while the bottom convolution layers were frozen with the pre‐trained weights on ImageNet; by loading the model trained in the first phase, the second phase retrained the model using the target dataset. The proposed procedure provides a significant increase in efficiency relative to other state‐of‐the‐art methods. It reaches an average accuracy of 99.78% in the public dataset with clear backdrops. Even under multiple classes and heterogeneous background conditions, the average accuracy realises 99.33% for identifying the rice disease types. The experimental findings show the feasibility and effectiveness of the proposed procedure.