A neural network approach for disease forecasting in grapes using weather parameters

Sannakki, Sanjeev S.; Rajpurohit, Vijay S.; Sumira, F.; Venkatesh, H.

doi:10.1109/icccnt.2013.6726613

Cited by 25 publications

(15 citation statements)

References 6 publications

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“…Being perceptive weather conditions in advanced is imperative for both individuals as well as organizations [3]. Accurate weather forecasts can tell an airport control tower what information needs to be sent to airplanes that are taking off or landing, it can tell a farmer the best time for cultivate various crops, and also predict natural calamities [10]. Humans have been looking to find out ways to forecast accurate weather conditions for centuries.…”

Section: Introductionmentioning

confidence: 99%

“…One more similar approach to forecasting is the Analogue Method; it is called so because it uses similarities between existing maps and analogous maps of weather from the past. For instance, assume if weather map for December 10,2002, is found to be nearly identical with a weather map for January 8,1993. Since the weather conditions for the earlier date is already known it can be assumed that similar weather patterns would be encountered on the later date.…”

Section: Introductionmentioning

confidence: 99%

“…Since ancient time, significant efforts have been made towards effective rain making and weather forecasting techniques. However, timely and accurate weather forecasts could significantly reduce the damage to crops with the use of effective adjustments [10]. Such weather forecasts could support and provide guidelines for long range seasonal planning and selection of crops which are best suited according to varying climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network

Tyagi¹,

Suran²,

Pattanaik³

2016

IJCA

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Temperature prediction is one of the most important and challenging task in today"s world. Temperature prediction is the attempt by meteorologists to forecast the state of the atmospheric parameters such as: Temperature, Humidity, etc. The paper presents research on weather forecasting by using historical dataset. Because atmosphere pattern is complex, nonlinear system, traditional methods aren"t effective and efficient. Artificial Neural Network is an influential method for resolving such problems. The proposed ANN evaluates the performance of the developed models by applying different neurons, hidden layers and transfer functions to predict temperature for 365 days of the year. The criteria used for appropriate model selection is mean square error (MSE). Contrary to similar researches the data model and workflow suggested in the paper generated lesser MSE (i.e. more accurate results) that too with reduced computational complexity (i.e. better performance).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network

Tyagi¹,

Suran²,

Pattanaik³

2016

IJCA

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“…The results provide prediction accuracy of 77.27% compared with regression algorithm 40.91%. [48] work with Feed Forward Neural Network for forecast the weather for prevent Downy Mildew, Powdery Mildew and Anthracnose in grapes crops, obtaining a correlation coefficient of 0.64 and 0.66 for rainfall and mean temperature respectively. Finally, [49] discover the apparition of Leafroller in kiwi crops through Bayesian Networks.…”

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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“…There is a hidden layer and many neurons between the input and the output layer. The proposed method [1] firstly forecasted the weather using the historical weather data with a help of two approaches…”

Section: ) Feed Forward Neural Networkmentioning

confidence: 99%

A Survey on Data Mining and Image Processing Techniques Used in Agriculture

Deshmukh¹

2018

IJRASET

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Data mining is used to extract the information from the large dataset and used to predict patterns and behavior of an application. Data mining plays a chief role in the fields of e-commerce, healthcare sector, and agricultural sector. Agriculture is the prime occupation in India. Crop productivity mainly depends on weather conditions. Data mining is used in agriculture to predict crop productivity, water management, crop disease management, pesticide recommendation by using different algorithms. Image processing techniques are also widely used to identify crop diseases using images of affected crops. The purpose of this survey is to study data mining and image processing techniques used in crop disease prediction and identification.

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A neural network approach for disease forecasting in grapes using weather parameters

Cited by 25 publications

References 6 publications

Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network

Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network

Toward detecting crop diseases and pest by supervised learning

A Survey on Data Mining and Image Processing Techniques Used in Agriculture

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