Environmentally friendly technologies for the prevention and control of crop diseases and insect pests are important to reduce the use of chemical pesticides, improve the quality of agricultural products, protect the environment, and promote sustainable development of crop production. On the basis of Internet of Things (IoT) technology, we developed a prevention and control system for crop diseases and insect pests with two main components: a plant protection device (the hardware) and an information management system (the software). To be suitable for both facility- and field-based production scenarios, we incorporated two types of plant protection devices, utilizing ozone sterilization and light-trap technologies. The devices were equipped with various sensors to realize real-time collection and monitoring of data on the crop production environment. The information management system has an IoT-based architecture and includes a mobile device app to enable remote control of the plant protection devices for intelligent management of plant protection data. The system can achieve efficient management of large-scale equipment applications and multi-device collaborative work to prevent and control pests and diseases. The developed system has operated successfully for several years in China and has been applied to cucumber, tomato, rice, and other crops. We demonstrate the effectiveness and practicality of the system in a greenhouse facility and in the field.

Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient.

Regular control of diseases and pests is crucial for maximizing cauliflower yield and quality. Spray application of chemical pesticides causes environmental pollution, pesticide residue accumulation, and is labor-intensive. To address these challenges, we developed an unmanned plant protection device integrating ozone sterilization, light traps, and Internet of Things (IoT) technologies. Installed in a greenhouse, the device included an ozone generator, high-speed fan, and insect traps. It was remotely controlled via a mobile app for real-time adjustments to ozone release, fan speed, trap lamp operation, environmental data collection, and system monitoring. Greenhouse experiments tested the device against cauliflower aphids, Pieris rapae, and black rot. Infestation/infection rates of aphids, Pieris rapae, and black rot in the greenhouse with the device were 23.18%, 19.72%, and 45.83%, respectively—22.52%, 7.21%, and 6.95% lower than the rates in the conventional greenhouse with pesticide sprays. No adverse effects on cauliflower growth were noted, and pesticide use was significantly reduced, lowering both agrochemical and labor costs. The results demonstrate that the unmanned device effectively controls pests and diseases and is safe for use. This offers a bio-friendly solution for pest control in cauliflower production.

To achieve fast and accurate identification of rice diseases in the field, we propose an automatic rice disease classifier, in which the process of characterizing rice diseases is visualized and analyzed by a deconvolutional neural network. An AlexNet model, pretrained by ImageNet, is constructed and trained on rice disease images to classify them. After the training is completed, the signal is repositioned to the corresponding position of the input image by a deconvolutional neural network corresponding to the AlexNet structure. The set of pixels that contribute most to the prediction of the convolutional neural network is identified from the deconvolution visualization map. The experimental results demonstrated the effectiveness of the proposed method. The classifier achieved an accuracy of 90.03% for the rice disease dataset, which was 8.39% and 16.78% higher than the accuracies achieved by the LeNet and BP neural networks, respectively. The features of the middle layer of the convolutional neural network perform a hierarchical transformation from low-level information, such as color, to high-level information, such as contours and edges of disease spots. This transformation process matches the criteria for the actual identification of rice diseases. The proposed method lays the foundation for the accurate identification of crop diseases and the design and adjustment of deep convolutional neural network structures.