Abstract 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.

The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be obtained by analysing the colour of the aquaculture water using image processing techniques. Therefore, this study proposes an intelligent monitoring approach for water quality. The critical features of water colour images are extracted, and then using the machine learning methods, an intelligent system for water quality monitoring is established based on the fused random vector functional link network (RVFL) and group method of data handling (GMDH) model. The proposed approach presents a superior performance relative to other state‐of‐the‐art methods, and it achieves an average predicting accuracy of 96.19% on the feature dataset. Experimental findings demonstrate the validity of the proposed approach, and it is accomplished efficiently for the monitoring of water quality.