Plant Disease Detection Using Image Processing and Machine Learning

Kulkarni, Pranesh; Karwande, Atharva; Kolhe, Tejas; Kamble, Soham; Joshi, A.; Wyawahare, Medha

doi:10.48550/arxiv.2106.10698

Cited by 10 publications

(17 citation statements)

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“…In this research, the SVM classifier predicts the diseases better than the minimum distance classifier. Kulkarni et al (2021) used two color features: gray level co-occurrence matrix and HSV conversion. Later, the metrics from the random forest algorithm show that the model achieved more than 87% accuracy for all five plants.…”

Section: Modelsmentioning

confidence: 99%

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

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Food security is a major concern in every developing country. Farmers face many problems while cultivating plants and they must take precautions at every stage of cultivation. Plants get diseases for various reasons like bacteria, insects, and fungus. Some diseases can be detected by examining the symptoms on the leaves. Early detection of diseases is a major concern and may require a thorough examination of the plants by an agricultural professional. This process is expensive and time taking. Machine learning (ML) algorithms help in image recognition and can be used to detect diseases on time without the need of an agricultural professional. In this project, the diseases in tomato leaves will be detected using image processing. The data from the images are extracted using different vectorization methods and classification algorithms like logistic regression (LR), support vector machine (SVM), and k‐nearest neighbors (KNN). Vectors of size 32 × 32 and 64 × 64 are used for training with normalizer scaling and no scaling. Out of the different approaches that were explored, SVM with the radial basis function (RBF) kernel gives the highest accuracy of 85% with no scaling and 64 × 64 image dimension.

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Section: Modelsmentioning

confidence: 99%

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

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“…Disease of Solanum trilobatum plant leaf has been identified in [67] using Graythresh in MATLAB. Researchers in [68] proposed a sophisticated method for crop disease identification using computer vision and machine learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The system developed in [68] produced an accuracy of 93% on 20 different plant illnesses. In another work, conducted in [69], an efficient approach for identification of plant illness in Malus Domestica using K-means clustering has been proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Mohammed

Yusoff²

2023

AEJ

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Prediction and classification of plant leaf illnesses by farmers using conventional approaches can be unexciting and erroneous. Problems may occur while trying to predict the sort of illnesses manually. Inability to detect diseases of plants promptly could lead to the destruction of crop plants and this can cause serious decline in the yield. Losses can be prevented and yield be maximized when farmers adopt computerized image processing approaches in their farms. Numerous techniques have been proposed and used in the prediction of diseases of crop plants based on the images of the infected leaves. Researchers have in the past achieved a lot in the aspect of plant illnesses identification by exploring several techniques and models. However, improvement needs to be provided on account of reviews, new advancements and discussions. Deploying technology can greatly enhance crop production across the globe. Different approaches and models can be trained with huge data to identify new improved methods for uncovering diseases of plants to tackle problem of low yield. Previous works have determined the robustness of various image processing techniques such as; K-means clustering, Naive Bayes, Feed forward neutral network (FFNN), Support Vector Machine (SVM), K-nearest neighbor (KNN) classifier, Fuzzy logic, Genetic Algorithm (GA), Artificial Neural Network (ANN), Convolutional Neural Network (CNN) etc. This paper provides a critical review and results of different types of approaches and methods used previously to detect and classify various types of plant leaf illnesses using image processing approaches.

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“…With the deep integration between deep learning and agricultural production, smart agriculture has become a major trend in the development of modern agriculture in different countries ( Kamilaris and Prenafeta-Boldú, 2018 ; Nguyen et al., 2020 ). The use of cameras mounted on hardware devices to determine whether leaves are infected by pathogens has been widely used in the field of smart agriculture, leading to the automatic identification of crop diseases ( Kulkarni et al., 2021 ; Gajjar et al., 2022 ). In recent years, increasing studies were focused in this field, Li et al.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOX-Tiny network for detection of tobacco brown spot disease

Lin

Yu²,

Pan³

et al. 2023

Front. Plant Sci.

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IntroductionTobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs.MethodsHere, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network.ResultsAs a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS).DiscussionTherefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.

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Plant Disease Detection Using Image Processing and Machine Learning

Cited by 10 publications

References 0 publications

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Improved YOLOX-Tiny network for detection of tobacco brown spot disease

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