Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm

Zhou, Bingyu; Xu, Jingwen; Zhao, Junfang; Li, Aiwen; Xia, Qiuyu

doi:10.2991/ic3me-15.2015.324

Cited by 10 publications

(3 citation statements)

References 2 publications

(1 reference statement)

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“…Though most of the techniques achieved good accuracy, in some paper [6], [7], [9] the amount of data is insufficient which could affect the model's training and the capacity to correctly recognize the diseases. Some studies [5]- [9], [14], [16] worked on very few diseases of cucumber. While some approaches, such as deep CNN, hyperspectral-imaging technology, CNN have good accuracy, they also have a high computational expense, hardware dependency, high cost.…”

Section: Resultsmentioning

confidence: 99%

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Cucumber disease recognition using machine learning and transfer learning

et al. 2021

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Cucumber is grown, as a cash crop besides it is one of the main and popular vegetables in Bangladesh. As Bangladesh's economy is largely dependent on the agricultural sector, cucumber farming could make economic and productivity growth more sustainable. But many diseases diminish the situation of cucumber. Early detection of disease can help to stop disease from spreading to other healthy plants and also accurate identifying the disease will help to reduce crop losses through specific treatments. In this paper, we have presented two approaches namely traditional machine learning (ML) and CNN-based transfer learning. Then we have compared the performance of the applied techniques to find out the most appropriate techniques for recognizing cucumber diseases. In our ML approach, the system involves five steps. After collecting the image, pre-processing is done by resizing, filtering, and contrast-enhancing. Then we have compared various ML algorithms using k-means based image segmentation after extracted 10 relevant features. Random forest gives the best accuracy with 89.93% in the traditional ML approach. We also studied and applied CNN-based transfer learning to investigate the further improvement of recognition performance. Lastly, a comparison among various transfer learning models such as InceptionV3, MobileNetV2, and VGG16 has been performed. Between these two approaches, MobileNetV2 achieves the highest accuracy with 93.23%.

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Section: Resultsmentioning

confidence: 99%

“…Zhou et al [5] have introduced the image preprocessing technique and SVM for classification. The accuracy of cucumber downy mildew in their system is 90.00%.…”

Section: Introductionmentioning

confidence: 99%

Cucumber disease recognition using machine learning and transfer learning

et al. 2021

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“…preprocessing the dataset, segmentation, feature extraction, classi cation, and evaluating the model's performance. Here, color-based lters and Gabor lters have been used for texture features of the tomato leaf.Zhou, Bingyu, et al [14] proposed a model SVM classi cation, In this after image processing SVM classi ed three types of cucumbers diseases i.e downy mildew, powdery mildew, and leaf rust and achieved 90% accuracy for identi cation of cucumber leaves disease.…”

Section: Cucumber Mosaicmentioning

confidence: 99%

A Novel approach for Cucumber Disease Detection (CDD) Using Deep Convolutional Neural Network

Singh

Kumar

2023

Preprint

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One of the main reasons for the loss of plant production is the interruption of photosynthesis, which is affected by disease of plant leaves. Powdery mildew is the main disease in cucumber plants which generally occur in the middle and late stages. Cucumber plant leaves are affected by various diseases such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; therefore it is important to identify the disease at the right time to prevent plant loss. Disease resistance traits play an important role in the investigation of plant breeders; therefore, it is important to determine the diseases of leaves immediately and keep in mind the area of their disease. Currently various machine learning algorithms have been used to detect plant diseases. Such as Support vector machine method, Self-organizing map and Convolution neural network respectively achieved 90%, 92% and 95% rate of accuracy to detect plant disease. In this work Deep-CNN-based disease detection model used to enhance the ResNet50 model’s and produce the most accurate results for cucumbers disease detection using data augmentation based on a separated data set. Proposed Cucumber Disease Detection (CDD) model has achieved 98% average accuracy of by using PyTorch library.

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