Support Vector Machine in Precision Agriculture: A review

Kok, Zhi Hong; Shariff, Abdul Rashid Mohamed; Alfatni, Meftah Salem M.; Khairunniza-Bejo, Siti

doi:10.1016/j.compag.2021.106546

Cited by 83 publications

(29 citation statements)

References 74 publications

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“…e calculation of mean average precision (mAP) depends on AP, where AP is defined as the intersection over union (IoU) threshold value of 0.5; for a class with N correctly identified samples, each correctly identified sample will correspond to a P b value, and the average of the N P b is taken to obtain the average accuracy of the class; see (10). mAP (IoU > 0.5) is defined as the mean value under all categories of AP, as shown in (11); this study has healthy leaves, early blight, and late blight. As the total number of detection categories, Q is 3, and mAP is the average cumulative value of the average accuracy of multicategory, which can overall demonstrate the comprehensive performance of the model.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In disease identification under natural conditions, traditional computer vision feature extraction is used as a critical technical link to extract colour, texture, and shape features by HSV colour space combined with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for disease identification. However, the diversity and complexity of leaf spots under actual conditions and the susceptibility of the features to light conditions, especially the poor stability of the colour features, make this method unsatisfactory for identification [9][10][11]. Compared with traditional methods, convolutional neural networks (CNN) are rapidly developing, and new types of models are emerging with more substantial expressive power in feature extraction [12,13], and VGG19, AlexNet, SqueezeNet, InceptionV3, Faster R-CNN, and ResNet50 have achieved better results in disease image detection and classification [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Dai

Fan

2022

Computational Intelligence and Neuroscience

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To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained. The results show that in the scenario of multiple complex factors interacting, the identification accuracy of early and late potato blight in this study reached 99.81%. The introduced data augmentation technique improved the average accuracy by 9.22%. Compared with the uncompressed YOLO v5 model, the integrated ActNN runs more efficiently, the accuracy loss due to compressed parameters is less than 0.65%, and the time consumption does not exceed 30 min, which saves a lot of computational cost and time. In summary, this research method can accurately identify potato early and late blight in various scenarios.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Dai

Fan

2022

Computational Intelligence and Neuroscience

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“…Support vector machine (SVM) modelling was also considered for use in this study but was discarded due to the improved interpretability of relationships formed within RF models. SVM models have often performed similarly to RF models (see review 70 ), but lack outputs that enable transparent interpretation of relationships formed within the model [71][72][73] .…”

Section: Methodsmentioning

confidence: 99%

Identifying links between monsoon variability and rice production in India through machine learning

Bowden

Foster

Parkes

2023

Sci Rep

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Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability.

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“…[62]. SVRs have been used for regression applications in agriculture [63][64][65][66]. Unlike the other CI models discussed earlier, SVRs do not have any strong parallels in nature.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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Support Vector Machine in Precision Agriculture: A review

Cited by 83 publications

References 74 publications

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Identifying links between monsoon variability and rice production in India through machine learning

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

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