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RSS FeedsRemote Sensing, Vol. 13, Pages 4218: High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module (Remote Sensing)

 
 

21 october 2021 11:29:41

 
Remote Sensing, Vol. 13, Pages 4218: High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module (Remote Sensing)
 


Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.


 
155 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 4216: Hexagonal Grid-Based Framework for Mobile Robot Navigation (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4217: Quasi Geoid and Geoid Modeling with the Use of Terrestrial and Airborne Gravity Data by the GGI Method—A Case Study in the Mountainous Area of Colorado (Remote Sensing)
 
 
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