Image Processing and Classification, A Method for Plant Disease Detecion

doi:10.35940/ijitee.i1139.0789s19

Cited by 5 publications

(3 citation statements)

References 6 publications

(6 reference statements)

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“…These patches gradually advance towards the leaf's veins, becoming more evident across the entire leaf blade, as depicted in Fig. 4 13 , 14 . The visual impact of these patches serves as a distressing indication of the escalating infection, signifying the relentless progression of the Banana Bunchy Top Virus.…”

Section: Banana Leaf Diseases and Their Significancementioning

confidence: 90%

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Analysis of banana plant health using machine learning techniques

Thiagarajan,

Kulkarni,

Jadhav

et al. 2024

Sci Rep

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The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

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Section: Banana Leaf Diseases and Their Significancementioning

confidence: 90%

“…The Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification and regression. The objective of the SVM algorithm is to classify data points in an N dimensional space by finding a hyperplane 14 . It is considered one of the best methods for both linear and nonlinear classification.…”

Section: Methodsmentioning

confidence: 99%

Analysis of banana plant health using machine learning techniques

Thiagarajan,

Kulkarni,

Jadhav

et al. 2024

Sci Rep

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“…It shows how the pattern of strength changes locally. Using the Colour Co-occurrence Matrix (CCM) method, texture analysis was used to pull out features from pictures in a training dataset [60]. The basic parts of CapsNet are capsules [61].…”

Section: Methods For Extracting the Features And Selecting The Featuresmentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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