Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks

Ishengoma, Farian S.; Idris, A.; Said, Rutabayiro Ngoga

doi:10.1016/j.compag.2021.106124

Cited by 54 publications

(24 citation statements)

References 24 publications

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“…In this work, TP and TN correspond to the correct identification of maize seeds, while FP and FN correspond to false identification of it. The performance of models was evaluated based on some statistical parameters of the confusion matrix, such as accuracy, sensitivity, specificity, precision, and F1-score, which can be obtained from them [33]. The performance evaluation was performed using images from the validation set and their respective labels, which were not used for training.…”

Section: Performance Evaluationmentioning

confidence: 99%

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Tan

Zhang

et al. 2022

Agriculture

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Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

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Section: Performance Evaluationmentioning

confidence: 99%

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Tan

Zhang

et al. 2022

Agriculture

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“…2022, 12, x FOR PEER REVIEW 3 of 16 management and decision-making processes [33]. Farian et al also used the corn leaves damaged by Spodoptera frugiperda and applied VGG16 and InceptionV3 to detect the infected corn leaves captured by the UAV (UAV) remote sensing technology while using the angular detection method in computer vision to strengthen the feature representation and improve the detection accuracy [34]. This paper presents a CNN-based deep learning system for the automatic detection of maize leaves infected by Spodoptera frugiperda; from RGB UAV remote sensing images at high spatial resolution.…”

Section: Study Areamentioning

confidence: 99%

“…Ana et al carry small aircraft RGB camera drones to obtain the vineyard plant image, and the application of geometric vision and computer vision technology, combined with landform factors on the influence of pests on the vineyard of the quantitative analysis for the farm digital management provides accurate low-cost information, which helps in the implementation and improvement of farm management and decision-making processes [33]. Farian et al also used the corn leaves damaged by Spodoptera frugiperda and applied VGG16 and InceptionV3 to detect the infected corn leaves captured by the UAV (UAV) remote sensing technology while using the angular detection method in computer vision to strengthen the feature representation and improve the detection accuracy [34].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Detection of Spodoptera frugiperda by Feeding Symptoms Directly from UAV RGB Imagery

et al. 2022

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The use of digital technologies to detect, position, and quantify pests quickly and accurately is very important in precision agriculture. Imagery acquisition using air-borne drones in combination with the deep learning technique is a new and viable solution to replace human labor such as visual interpretation, which consumes a lot of time and effort. In this study, we developed a method for automatic detecting an important maize pest—Spodoptera frugiperda—by its gnawing holes on maize leaves based on convolution neural network. We validated the split-attention mechanism in the classical network structure ResNet50, which improves the accuracy and robustness, and verified the feasibility of two kinds of gnawing holes as the identification features of Spodoptera frugiperda invasion and the degree. In order to verify the robustness of this detection method against plant morphological changes, images at the jointing stage and heading stage were used for training and testing, respectively. The performance of the models trained with the jointing stage images has been achieved the validation accuracy of ResNeSt50, ResNet50, EfficientNet, and RegNet at 98.77%, 97.59%, 97.89%, and 98.07%, with a heading stage test accuracy of 89.39%, 81.88%, 86.21%, and 84.21%.

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“…Kerkech et al, identified vine diseases using UAV-based RGB images [12]. Ishengoma et al, identified maize leaves infected by fall armyworms using UAV-based RGB images [13]. RGB images can provide rich color and texture features due to their relatively higher spatial resolution, but the spectral information provided is limited as the band number is only three, the band wavelength is mainly in the visible range, and the band width is wide.…”

Section: Introductionmentioning

confidence: 99%

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

Zhang

et al. 2022

Remote Sensing

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Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

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Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks

Cited by 54 publications

References 24 publications

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Autonomous Detection of Spodoptera frugiperda by Feeding Symptoms Directly from UAV RGB Imagery

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

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