Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad, Aeri; Fuad, Muhammad; Rochman, Eka Mala Sari

doi:10.18280/mmep.100220

Cited by 6 publications

(2 citation statements)

References 22 publications

(25 reference statements)

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“…They obtained an accuracy of 96.2% for the examined data. Rachmad et al [21] used several models of CNNs, like SqueezeNet, AlexNet, Resnet-101, ResNet50, and Resnet18, to classify diseases in corn plants, obtaining 95.59% accuracy with the ResNet50 CNN model. Additionally, UAVs and CNNs can be used in combination to create systems for the detection of weeds, like in the study presented by Haq et al [22], which proposed a CNN with a learning vector quantization algorithm to classify weeds present in different crops such as soybean, grass, soil, and broadleaf, obtaining an overall accuracy of 99.44%.…”

Section: Introductionmentioning

confidence: 99%

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

López-Martínez,

Díaz-Flórez,

Villagrana-Barraza

et al. 2024

Agronomy

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Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture.

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Section: Introductionmentioning

confidence: 99%

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

López-Martínez,

Díaz-Flórez,

Villagrana-Barraza

et al. 2024

Agronomy

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“…In recent years, Convolutional Neural Networks (CNN) have experienced rapid development and made a positive impression on the field of image classification [127] and robot vision [128]. In the work [129], CNN was used for image recognition of the US postal handwritten digit dataset.…”

Section: Introductionmentioning

confidence: 99%

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

Fuad,

Umam,

Wahyuni

et al. 2023

Journal of Robotics and Control

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Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

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Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique

Bachhal,

Kukreja,

Ahuja

et al. 2024

Sci Rep

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The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

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Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Cited by 6 publications

References 22 publications

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique

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