BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier

Singh, Ashutosh Kumar; Chourasia, Bharti; Raghuwanshi, Neetesh; Raju, K. Venkata

doi:10.14445/22315381/ijett-v69i4p206

Cited by 3 publications

(2 citation statements)

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“…17 Individual predictions made by decision trees were not so accurate, but the combined result was close to the actual value. Color moments, Gabor wavelet, and Harris Corner-based feature extraction methods were implemented by Singh et al 18 This was followed by binary particle swarm optimization-based feature selection with a random forest classifier.…”

Section: Machine Learning-based Approachesmentioning

confidence: 99%

A novel deep learning architecture for disease classification in Arabica coffee plants

Ramamurthy

Thekkath

Batra

et al. 2023

Concurrency and Computation

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Several research works on disease detection in coffee plants have been presented in recent years. Leaf miner and rust are the most prevalent diseases in Arabica coffee plants. Early detection of such diseases allows farmer to take diagnostic actions before the infection spreads to neighboring plants. With advancements in drones and artificial intelligence (AI), the automatic detection of leaf diseases is gaining prominence in the field of smart agriculture. Furthermore, it is critical to develop an accurate method for infestation detection with minimal computational complexity. Existing works for plant disease detection utilize pre-trained deep learning models with millions of parameters.A feasible trade-off has to be attained between accuracy and computational complexity for the deployment of such deep networks. This research proposes an effective method for disease detection in Arabica coffee plants using EfficientNetB0 architecture. The architecture of the EfficientNetB0 network was improvised by including a ghost module at its end. This integration allows the network to learn effectively with minimal parameters without compensating for the end accuracy. The proposed model has a total of 4,874,531 parameters which is significantly lesser than most of the state-of-the-art deep learning architectures and achieved an accuracy of 84%.

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Section: Machine Learning-based Approachesmentioning

confidence: 99%

A novel deep learning architecture for disease classification in Arabica coffee plants

Ramamurthy

Thekkath

Batra

et al. 2023

Concurrency and Computation

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show abstract

“…The problem of ambiguity in the feature splitting, known as the discretization process, can be found in many datasets (Ferreira et al, 2015) , including digital image data (Singh et al, 2021;Sutha et al, 2021;Resti et al, 2020). Since each feature in an image has a unique pixel value interval, separating digital image data processed using the RGB color space model introduces uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Resti

Irsan

Amini

et al. 2022

Sci. Technol. Indones

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Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

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Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect

Hashim,

Ali,

Mahadi

et al. 2024

Rice Science

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BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier

Cited by 3 publications

References 0 publications

A novel deep learning architecture for disease classification in Arabica coffee plants

A novel deep learning architecture for disease classification in Arabica coffee plants

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect

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