Toward detecting crop diseases and pest by supervised learning

Corrales, David Camilo; Corrales, Juan Carlos; Casas, Apolinar Figueroa

doi:10.11144/javeriana.iyu19-1.tdcd

Cited by 36 publications

(28 citation statements)

References 25 publications

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“…The selection criteria of these classifiers are based on previous surveys which show that are the most suitable for classification and predictions tasks [35,36], especially in the detection of crops diseases and pest [37]. With the dataset introduced in Section 2.1, we used a 10-fold cross validation to estimate the scores reported in the following figure and tables.…”

Section: Selection Of Base Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

Corrales

Casas

Ledezma

et al. 2015

Computational Science and Its Applications -- ICCSA 2015

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Abstract. Rust is a disease that leads to considerable losses in the worldwide coffee industry. In Colombia, the disease was first reported in 1983 in the department of Caldas. Since then, it spread rapidly through all other coffee departments in the country. Recent research efforts focus on detection of disease incidence using computer science techniques such as supervised learning algorithms. However, a number of different authors demonstrate that results are not sufficiently accurate using a single classifier. Authors in the computer field propose alternatives for this problem, making use of techniques that combine classifier results. Nevertheless, the traditional approaches have a limited performance due to dataset absence. Therefore we proposed an empirical multiclassifier for coffee rust detection in Colombian crops.

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Section: Selection Of Base Classifiersmentioning

confidence: 99%

“…We tested the most relevant algorithms of supervised learning for classification tasks as Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Naive Bayes (NB), C4.5 Decision Tree, and K nearest neighbors (K-NN) [37] to choose the classifier -1st level, computing precision, recall and F-measure as seen in Table 3. …”

Section: Classifier -1st Levelmentioning

confidence: 99%

An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

Corrales

Casas

Ledezma

et al. 2015

Computational Science and Its Applications -- ICCSA 2015

Self Cite

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“…For classifiers selection, there were taken 4 researches as a starting point [18,[44][45][46], in which performing a literature review and theoretically evaluate supervised learning algorithms most commonly used as the case of Decision Trees (DT), Artificial Neural Networks (ANN), Bayesian Networks (BN), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) considering metrics as: accuracy, noise tolerance, ability of explanation, learning speed and classification speed.…”

Section: Classifiers For Water Quality Detectionmentioning

confidence: 99%

“…SL tasks predict or classify a new input data from examples (instances), commonly called training data (composed of attributes and a target variable), through algorithms such as decision trees (DT), Bayesian networks (BN), Artificial Neural Networks (ANN), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) [18]. However, these research approaches set aside the data quality verification (i.e.…”

Section: Introductionmentioning

confidence: 99%

Data Processing for a Water Quality Detection System on Colombian Rio Piedras Basin

Castillo

Corrales

Lasso

et al. 2016

Computational Science and Its Applications -- ICCSA 2016

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Abstract. Freshwater is considered one of the most important of planet's renewable natural resources. In this sense, it is vital to study and evaluate the water quality in rivers and basins. A study area is Rio Piedras Basin, which is the main water supplier source of 9 rural communities in Colombia. Nevertheless, these communities do not make a water quality control. Different research has been conducted to develop water quality detection systems through supervised learning algorithms. However, these research approaches set aside the data processing for improve the outcomes of supervised learning algorithms. This paper presents an improvement of data processing techniques for a water quality detection system based on supervised learning and data quality techniques for Rio Piedras Basin.

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“…DavidCamilo Corrales et al, [4] defined the detection of diseases and pests in different crops using supervised learning algorithms. The Research and algorithms were compared in order to observe the performance.…”

Section: Machine Learning Algorithms For Pest Managementmentioning

confidence: 99%

Pest Management using Machine Learning Algorithms, A Review

Durgabai¹

2018

IJCSEITR

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Agriculture is a unique business crop production, which is dependent on many climate and economy factors, the major occupation of Indians is farming where in about 70% of the population depends on agriculture. Farmers have wide range of diversity to select suitable Fruit and Vegetable crops. However, the cultivation of these crops for optimum yield and quality produce is highly technical. The crop production has reduced due to various factors like pest attack, diseases and climatic conditions. Crop protection is the science and practice of managing pests, plant diseases and other pest organisms that damage agricultural crops. Machine learning is a looming field of computer science which can be applied to the farming sector quite effectively. It can facilitate the up-gradation of conventional farming techniques in the most cost-friendly approach. This paper reviews on how different machine learning algorithms are useful in pest management of various crops.

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Toward detecting crop diseases and pest by supervised learning

Cited by 36 publications

References 25 publications

An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

Data Processing for a Water Quality Detection System on Colombian Rio Piedras Basin

Pest Management using Machine Learning Algorithms, A Review

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