Data mining and wireless sensor network for agriculture pest/disease predictions

Tripathy, Amiya Kumar; Adinarayana, J.; Sudharsan, D.; Merchant, S. N.; Desai, U.B.; Vijayalakshmi, K.; Reddy, D. Sreekantha; Sreenivas, G.; Ninomiya, S.; Hirafuji, Masayuki; Kiura, Takuji; Tanaka, Kei

doi:10.1109/wict.2011.6141424

Cited by 55 publications

(40 citation statements)

References 3 publications

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“…Jiangui Liuet al, [16] Inverted Gaussian Model is applied to study the relationships between crop variables and the red edge parameters. A. K. Tripathy et al, [17] conducted an experiment in a semi-arid region of India to understand the crop-weather-pest relations using wireless sensors and field-level surveillance data on the groundnut pest Trips. Data mining techniques were used to turn the data into useful information and correlation of crop-weather-pest field.…”

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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Section: Machine Learning Algorithms For Pest Managementmentioning

confidence: 99%

Pest Management using Machine Learning Algorithms, A Review

Durgabai¹

2018

IJCSEITR

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“…A Indo-Japan initiative [44], [45] used Bayesian Networks (BN) in peanut crops of India for monitoring of pest and disease. First BN is utilized to assume the presence (or absence) of a particular feature in dataset that describe the thrip pest.…”

Section: Related Workmentioning

confidence: 99%

Toward detecting crop diseases and pest by supervised learning

Corrales

Casas

2015

IyU

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The climate change has caused threats to agricultural production; the extremes of temperature and humidity, and other abiotic stresses are contributing factors to the etiology of disease and pest on crops. About the matter, recent research efforts have focused on predicting disease and pest crops using techniques such as supervised learning algorithms. Therefore in this paper, we present an overview of supervised learning algorithms commonly used in agriculture for the detection of pests and diseases in crops such as corn, rice, coffee, mango, peanut, and tomato, among others, with the aim of selecting the algorithms that give the best performance for the agricultural sector.

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“…In particular, Bayesian techniques have been adopted (Tripathy et al, 2011;Pérez-Ariza et al, 2012). On the other hand, support vector machines are also extensively used (Wang & Ma, 2011).…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to the prediction of citrus production

Dı́az

Mazza

Combarro

et al. 2017

Span. j. agric. res.

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An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

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Data mining and wireless sensor network for agriculture pest/disease predictions

Cited by 55 publications

References 3 publications

Pest Management using Machine Learning Algorithms, A Review

Pest Management using Machine Learning Algorithms, A Review

Toward detecting crop diseases and pest by supervised learning

Machine learning applied to the prediction of citrus production

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