Prediction of early blight severity in tomato (Solanum lycopersicum) by machine learning technique

Paul, Ranjit Kumar; Vennila, S.; Bhat, M. Narayana; Yadav, Satish Kumar; Sharma, Vipin Kumar; Nisar, Shabistana; Panwar, Sanjeev

doi:10.56093/ijas.v89i11.95344

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Linear Regression is a basic statistical method that draws a best-fitting regression line through real and predicted points. 25 It is frequently used in prediction problems, 16,[26][27][28] especially on datasets that have linear correlation between attributes.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Prediction of cancer incidence rates for the European continent using machine learning models

Şekeroğlu

Tuncal

2021

Health Informatics J

View full text Add to dashboard Cite

Cancer is one of the most important and common public health problems on Earth that can occur in many different types. Treatments and precautions are aimed at minimizing the deaths caused by cancer; however, incidence rates continue to rise. Thus, it is important to analyze and estimate incidence rates to support the determination of more effective precautions. In this research, 2018 Cancer Datasheet of World Health Organization (WHO), is used and all countries on the European Continent are considered to analyze and predict the incidence rates until 2020, for Lung cancer, Breast cancer, Colorectal cancer, Prostate cancer and All types of cancer, which have highest incidence and mortality rates. Each cancer type is trained by six machine learning models namely, Linear Regression, Support Vector Regression, Decision Tree, Long-Short Term Memory neural network, Backpropagation neural network, and Radial Basis Function neural network according to gender types separately. Linear regression and support vector regression outperformed the other models with the [Formula: see text] scores 0.99 and 0.98, respectively, in initial experiments, and then used for prediction of incidence rates of the considered cancer types. The ML models estimated that the maximum rise of incidence rates would be in colorectal cancer for females by 6%.

show abstract

Section: Machine Learning Modelsmentioning

confidence: 99%

Prediction of cancer incidence rates for the European continent using machine learning models

Şekeroğlu

Tuncal

2021

Health Informatics J

View full text Add to dashboard Cite

show abstract

“…However, plant disease resistance needs to be faced by all crops in large-scale production, and various diseases can damage crops in any given growing season, resulting in reduced yield or lower quality. For example, early blight, a fungal disease that is a common type of disease in field-grown tomatoes, causes round or elongated brown lesions to form on infected plant tissue ( Paul et al., 2019 ; Hu and Zhang, 2023 ). Late blight causes infected leaves to turn pale green to brown and eventually to wither and die ( Arafa et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A ResNet50-DPA model for tomato leaf disease identification

Liang,

Jiang

2023

Front. Plant Sci.

View full text Add to dashboard Cite

Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which the method based on the convolutional neural network is effective. While it is challenging to capture key features or tends to lose a large number of features when extracting image features by applying this method, resulting in low accuracy of disease identification. Therefore, the ResNet50-DPA model is proposed to identify tomato leaf diseases in the paper. Firstly, an improved ResNet50 is included in the model, which replaces the first layer of convolution in the basic ResNet50 model with the cascaded atrous convolution, facilitating to obtaining of leaf features with different scales. Secondly, in the model, a dual-path attention (DPA) mechanism is proposed to search for key features, where the stochastic pooling is employed to eliminate the influence of non-maximum values, and two convolutions with one dimension are introduced to replace the MLP layer for effectively reducing the damage to leaf information. In addition, to quickly and accurately identify the type of leaf disease, the DPA module is incorporated into the residual module of the improved ResNet50 to obtain an enhanced tomato leaf feature map, which helps to reduce economic losses. Finally, the visualization results of Grad-CAM are presented to show that the ResNet50-DPA model proposed can identify diseases more accurately and improve the interpretability of the model, meeting the need for precise identification of tomato leaf diseases.

show abstract

Dynamic ensemble-based machine learning models for predicting pest populations

Singh,

Yeasin,

Paul

et al. 2024

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.

show abstract

Prediction of early blight severity in tomato (Solanum lycopersicum) by machine learning technique

Cited by 4 publications

References 7 publications

Prediction of cancer incidence rates for the European continent using machine learning models

Prediction of cancer incidence rates for the European continent using machine learning models

A ResNet50-DPA model for tomato leaf disease identification

Dynamic ensemble-based machine learning models for predicting pest populations

Contact Info

Product

Resources

About