Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab(Venturia inaequalis)disease in commercial orchards.Near-infrared(NIR)im...Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab(Venturia inaequalis)disease in commercial orchards.Near-infrared(NIR)imagery can display apple scab symptoms earlier and at a greater severity than visible-spectrum(RGB)imagery.Early apple scab diagnosis based on NIR imagery may be automated using deep learning convolutional neural networks(CNNs).CNN models have previously been used to classify a range of apple diseases accurately but have primarily focused on identifying late-stage rather than early-stage detection.This study fine-tunes CNN models to classify apple scab symptoms as they progress from the early to late stages of infection using a novel multispectral(RGB-NIR)time series created especially for this purpose.This novel multispectral dataset was used in conjunction with a large Apple Disease Identification(ADID)dataset created from publicly available,pre-existing disease datasets.This ADID dataset contained 29,000 images of infection symptoms across six disease classes.Two CNN models,the lightweight MobileNetV2 and heavyweight EfficientNetV2L,were fine-tuned and used to classify each disease class in a testing dataset,with performance assessed through metrics derived from confusion matrices.The models achieved scab-prediction accuracies of 97.13%and 97.57%for MobileNetV2 and EfficientNetV2L,respectively,on the secondary data but only achieved accuracies of 74.12%and 78.91%when applied to the multispectral dataset in isolation.These lower performance scores were attributed to a higher proportion of false-positive scab predictions in the multispectral dataset.Time series analyses revealed that both models could classify apple scab infections earlier than the manual classification techniques,leading to more false-positive assessments,and could accurately distinguish between healthy and infected samples up to 7 days post-inoculation in NIR imagery.展开更多
In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticr...In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the accuracy.The performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.展开更多
Dithianon is a multi-site fungicide and has never been object of suspects and reports of reduced sensitivity and activity. Italian IFP technicians had the suspect of reductions of activity by this fungicide on </sp...Dithianon is a multi-site fungicide and has never been object of suspects and reports of reduced sensitivity and activity. Italian IFP technicians had the suspect of reductions of activity by this fungicide on </span><i><span style="font-family:Verdana;">Venturia inaequalis.</span></i><span style="font-family:Verdana;"> Methodologies, </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> were carried out to verify this suspect. Populations poorly controlled with suspects on dithianon and sensible ones </span><span><span style="font-family:Verdana;">were utilized. The tests </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> permitted to evidence light and non-significant</span></span><span style="font-family:Verdana;"> reductions of sensitivity of poorly controlled populations </span></span><span style="font-family:Verdana;">with </span><span style="font-family:Verdana;">respect </span><span style="font-family:Verdana;">to </span><span style="font-family:""><span style="font-family:Verdana;">sensible ones. </span><i><span style="font-family:Verdana;">In vivo tests</span></i><span style="font-family:Verdana;"> on seedlings were non</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">reliable for a general low activity of dithianon. On the contrary, the original </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> methodology on grafted apple plants showed several reductions of activity, with moderate levels and a spot distribution in orchards. The cause was probably due to the increase</span></span><span style="font-family:Verdana;">d</span><span style="font-family:Verdana;"> treatments with dithianon caused by problems on other groups of fungicides and by a high infective pressure in some years. It is discussed if this reduction can be considered a resistance phenomenon or a temporary modification of the interactions </span><span style="font-family:Verdana;">of </span><span style="font-family:Verdana;">plant-fungus-fungicide.展开更多
基金funded by the Biotechnology and Biological Sciences Research Council under grant BB/T508950/1 as part of the Waitrose Collaborative Training Partnership and conducted at Lancaster University.
文摘Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab(Venturia inaequalis)disease in commercial orchards.Near-infrared(NIR)imagery can display apple scab symptoms earlier and at a greater severity than visible-spectrum(RGB)imagery.Early apple scab diagnosis based on NIR imagery may be automated using deep learning convolutional neural networks(CNNs).CNN models have previously been used to classify a range of apple diseases accurately but have primarily focused on identifying late-stage rather than early-stage detection.This study fine-tunes CNN models to classify apple scab symptoms as they progress from the early to late stages of infection using a novel multispectral(RGB-NIR)time series created especially for this purpose.This novel multispectral dataset was used in conjunction with a large Apple Disease Identification(ADID)dataset created from publicly available,pre-existing disease datasets.This ADID dataset contained 29,000 images of infection symptoms across six disease classes.Two CNN models,the lightweight MobileNetV2 and heavyweight EfficientNetV2L,were fine-tuned and used to classify each disease class in a testing dataset,with performance assessed through metrics derived from confusion matrices.The models achieved scab-prediction accuracies of 97.13%and 97.57%for MobileNetV2 and EfficientNetV2L,respectively,on the secondary data but only achieved accuracies of 74.12%and 78.91%when applied to the multispectral dataset in isolation.These lower performance scores were attributed to a higher proportion of false-positive scab predictions in the multispectral dataset.Time series analyses revealed that both models could classify apple scab infections earlier than the manual classification techniques,leading to more false-positive assessments,and could accurately distinguish between healthy and infected samples up to 7 days post-inoculation in NIR imagery.
基金This work was supported by Taif University Researchers Supporting Project(TURSP)under number(TURSP-2020/73),Taif University,Taif,Saudi Arabia.
文摘In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the accuracy.The performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.
文摘Dithianon is a multi-site fungicide and has never been object of suspects and reports of reduced sensitivity and activity. Italian IFP technicians had the suspect of reductions of activity by this fungicide on </span><i><span style="font-family:Verdana;">Venturia inaequalis.</span></i><span style="font-family:Verdana;"> Methodologies, </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> were carried out to verify this suspect. Populations poorly controlled with suspects on dithianon and sensible ones </span><span><span style="font-family:Verdana;">were utilized. The tests </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> permitted to evidence light and non-significant</span></span><span style="font-family:Verdana;"> reductions of sensitivity of poorly controlled populations </span></span><span style="font-family:Verdana;">with </span><span style="font-family:Verdana;">respect </span><span style="font-family:Verdana;">to </span><span style="font-family:""><span style="font-family:Verdana;">sensible ones. </span><i><span style="font-family:Verdana;">In vivo tests</span></i><span style="font-family:Verdana;"> on seedlings were non</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">reliable for a general low activity of dithianon. On the contrary, the original </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> methodology on grafted apple plants showed several reductions of activity, with moderate levels and a spot distribution in orchards. The cause was probably due to the increase</span></span><span style="font-family:Verdana;">d</span><span style="font-family:Verdana;"> treatments with dithianon caused by problems on other groups of fungicides and by a high infective pressure in some years. It is discussed if this reduction can be considered a resistance phenomenon or a temporary modification of the interactions </span><span style="font-family:Verdana;">of </span><span style="font-family:Verdana;">plant-fungus-fungicide.
