Du.cn (P.S.) Correspondence: [email protected]: Maize leaf
Du.cn (P.S.) Correspondence: [email protected]: Maize leaf disease detection is an important 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 illness, aiming to raise the accuracy of standard artificial intelligence methods. Because the disease dataset was insufficient, this paper adopts image pre-processing techniques to extend and augment the disease samples. This paper uses transfer understanding and warm-up method to accelerate the instruction. Consequently, three kinds of maize illnesses, such as maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy on the proposed technique in the validation set reached 97.41 . This paper carried out a baseline test to verify the effectiveness with the proposed technique. 1st, 3 groups of CNNs with the most effective performance have been selected. Then, ablation experiments had been conducted on five CNNs. The results indicated that the performances of CNNs happen to be improved by adding the MAF module. Also, the combination of Sigmoid, ReLU, and Mish showed the ideal functionality on ResNet50. The accuracy is often improved by 2.33 , proving that the model proposed in this paper is often properly applied to agricultural production.Citation: Zhang, Y.; Wa, S.; Liu, Y.; Zhou, X.; Sun, P.; Ma, Q. High-Accuracy Detection of Maize Leaf Ailments CNN Based on Multi-Pathway Activation Function Module. Remote Sens. 2021, 13, 4218. https://doi.org/10.3390/rs13214218 Academic Editor: Adel Hafiane Received: 17 September 2021 Accepted: 18 Pyrroloquinoline quinone Autophagy 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. Also to food for humans, maize is definitely an excellent feed for animal husbandry. On top of that, it can be an essential raw material for the light market and healthcare market. Illnesses are the primary disaster affecting maize production, and also the annual loss triggered by disease is 60 . According to statistics, you’ll find greater than 80 maize illnesses worldwide. At present, some illnesses which include sheath blight, rust, northern leaf blight, curcuma leaf spot, stem base rot, head smut, etc., occur broadly and trigger significant consequences. Amongst these illnesses, the lesions of sheath blight, rust, northern leaf blight are discovered in maize leaves, whose qualities are apparent. For these ailments, rapid and Perospirone Data Sheet Correct detection is crucial to improve yields, which will help monitor the crop and take timely action to treat the diseases. With the improvement of machine vision and deep learning technologies, machine vision can rapidly and accurately identify these maize leaf ailments. Correct detection of maize leaf lesions would be the vital step for the automatic identification of maize leaf ailments. On the other hand, utilizing machine vision technologies to identify maize leaf ailments is difficult. Because the appearance of maize leaves, for instance shape, size, texture, and posture, varies significantly involving maize varieties and stages of growth. Growth edges of maize leaves are extremely irregular, plus the colour on the stem is related to that of the leaves. Various maize organs and plants block each other within the actual field atmosphere. The organic light is nonuniform and regularly altering, increasingPublisher’s.