Share this post on: (P.S.) Correspondence: [email protected]: Maize leaf illness detection is an crucial project inside 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 boost the accuracy of 3-Deazaneplanocin A Technical Information classic artificial intelligence strategies. Since the disease dataset was insufficient, this paper adopts image pre-processing techniques to extend and augment the disease samples. This paper uses transfer finding out and warm-up process to accelerate the training. Consequently, 3 types of maize diseases, including maculopathy, rust, and blight, might be detected efficiently and accurately. The accuracy on the proposed process inside the validation set reached 97.41 . This paper carried out a baseline test to verify the effectiveness of the proposed method. Very first, three groups of CNNs with the greatest overall performance were selected. Then, ablation experiments have been carried out on five CNNs. The results indicated that the performances of CNNs have already been improved by adding the MAF module. Moreover, the combination of Sigmoid, ReLU, and Mish showed the ideal performance on ResNet50. The accuracy might be improved by 2.33 , proving that the model proposed in this paper could be well applied to agricultural production.Citation: Zhang, Y.; Wa, S.; Liu, Y.; Zhou, X.; Sun, P.; Ma, Q. High-Accuracy Detection of Maize Leaf Illnesses CNN Based on Multi-Pathway Activation Function Module. Remote Sens. 2021, 13, 4218. Academic Editor: Adel Hafiane Received: 17 September 2021 Accepted: 18 October 2021 Published: 21 OctoberKeywords: maize leaf disease detection; activation functions; generative adversarial network; convolutional neural network1. Introduction Maize belongs to Gramineae, whose cultivated region and total output rank third only to wheat and rice. Furthermore to meals for humans, maize is an outstanding feed for animal husbandry. Moreover, it truly is a crucial raw material for the light industry and medical market. Ailments are the major disaster affecting maize production, plus the annual loss triggered by illness is 60 . Based on statistics, you will find more than 80 maize diseases worldwide. At present, some illnesses such as sheath blight, rust, northern leaf blight, curcuma leaf spot, stem base rot, head smut, etc., occur extensively and cause serious consequences. Among these diseases, the lesions of sheath blight, rust, northern leaf blight are discovered in maize leaves, whose characteristics are apparent. For these illnesses, rapid and precise detection is essential to improve yields, which can help monitor the crop and take timely action to treat the illnesses. Using the KL1333 MedChemExpress development of machine vision and deep understanding technologies, machine vision can swiftly and accurately determine these maize leaf diseases. Accurate detection of maize leaf lesions would be the critical step for the automatic identification of maize leaf diseases. Even so, applying machine vision technologies to identify maize leaf illnesses is complex. Since the appearance of maize leaves, like shape, size, texture, and posture, varies substantially among maize varieties and stages of development. Development edges of maize leaves are hugely irregular, plus the colour of the stem is comparable to that in the leaves. Distinctive maize organs and plants block one another in the actual field atmosphere. The natural light is nonuniform and regularly changing, increasingPublisher’s.

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