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Late Blight Prediction and Analysis

Bengtsson, Lukas (2017) In Bachelor's Theses in Mathematical Sciences MASM01 20171
Mathematical Statistics
Abstract
Phytophthora infestans cause severe losses in potato cultivation. Currently
one of the most effective ways to reduce the related loss is using fungicides. For these to have optimal effect they must be applied approximately 10 days before the first visual sign of late blight. As the appearance of late blight is hard to predict there is heavy overcompensation of fungicides nowadays. By better forecasts less chemical compounds will be used lowering the environmental impact. It may also increase the profit from potato cultivation as less money goes to fungicides.

There are several models trying to predict the late blight appearance date of
which one of the most well known is SIMCAST. Also Cox regression models have recently been looked... (More)
Phytophthora infestans cause severe losses in potato cultivation. Currently
one of the most effective ways to reduce the related loss is using fungicides. For these to have optimal effect they must be applied approximately 10 days before the first visual sign of late blight. As the appearance of late blight is hard to predict there is heavy overcompensation of fungicides nowadays. By better forecasts less chemical compounds will be used lowering the environmental impact. It may also increase the profit from potato cultivation as less money goes to fungicides.

There are several models trying to predict the late blight appearance date of
which one of the most well known is SIMCAST. Also Cox regression models have recently been looked into. Is it possible to improve these models?
Moreover what financial impact will it have for farmers?

First an empirical investigation of the data was conducted for a general
understanding of the subject. Then it was analyzed using, linear regression,
Cox regression, Elastic net and cross-validation. To fill in missing data imputation was used.

The best performing model was obtained through Cox regression using
SIMCAST and planting day as covariates. It had approximately
the same failure ratio as foliage prediction which is currently used, but had
23% more accurate predictions and lower standard deviation. SIMCAST
was principally out-performed by all other models. Using elastic net it was
possible to obtain models which was approximately as good as the best
model. Using the best model savings around 422 SEK/ha can be made from
reduced fungicides alone.

The newly obtained models has potential to increase the profit of potato
cultivation. Also by these the accuracy of prediction would contribute to
lower environmental impact. Notable is that the data used is not optimal
for the experiment as some had to be estimated etc. hence before relying
on these models field experiments should be considered. (Less)
Popular Abstract
Potato, potato as long as they lie on the dinner table. Potatoes are important but only a few know about pesticides which are used in the cultivation. A major factor which the corps need protection against is late blight (caused by Phytophthora infestans). For optimal effect of the pesticides they must be applied approximately 10 days before the first visual sign of late blight. As the appearance of late blight is hard to predict, there is heavy overcompensation of fungicides nowadays. By better forecasts less chemical compounds will be used lowering the environmental impact. It may also increase the profit from potato cultivation as less money goes to fungicides.

There are several models trying to predict the late blight appearance... (More)
Potato, potato as long as they lie on the dinner table. Potatoes are important but only a few know about pesticides which are used in the cultivation. A major factor which the corps need protection against is late blight (caused by Phytophthora infestans). For optimal effect of the pesticides they must be applied approximately 10 days before the first visual sign of late blight. As the appearance of late blight is hard to predict, there is heavy overcompensation of fungicides nowadays. By better forecasts less chemical compounds will be used lowering the environmental impact. It may also increase the profit from potato cultivation as less money goes to fungicides.

There are several models trying to predict the late blight appearance date of which one of the most well-known is SIMCAST. Also Cox regression models have recently been looked into. Is it possible to improve these models? Moreover what financial impact will it have for farmers? First an empirical investigation of the data was conducted for a general understanding of the subject. Then it was analysed using, linear regression, Cox regression, Elastic net and cross-validation. To fill in missing data imputation was used . The best performing model found had 23% more accurate predictions and lower standard deviation then those currently used. SIMCAST was principally out-performed by all other considered models. Using elastic net it was possible to obtain models which was approximately as good as the best model. Using the best model savings around 422 SEK/ha can be made from reduced fungicides alone.

The newly obtained models has potential to increase the profit of potato cultivation. Also by these the accuracy of prediction would contribute to lower environmental impact. Notable is that the data used is not optimal for the experiment as some had to be estimated etc. hence before relying on these models field experiments should be considered. (Less)
Please use this url to cite or link to this publication:
author
Bengtsson, Lukas
supervisor
organization
course
MASM01 20171
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Bachelor's Theses in Mathematical Sciences
report number
LUNFMS-3072-2017
other publication id
2017:E47
language
English
id
8919371
date added to LUP
2017-06-28 10:32:48
date last changed
2019-07-12 11:21:06
@misc{8919371,
  abstract     = {Phytophthora infestans cause severe losses in potato cultivation. Currently
one of the most effective ways to reduce the related loss is using fungicides. For these to have optimal effect they must be applied approximately 10 days before the first visual sign of late blight. As the appearance of late blight is hard to predict there is heavy overcompensation of fungicides nowadays. By better forecasts less chemical compounds will be used lowering the environmental impact. It may also increase the profit from potato cultivation as less money goes to fungicides.

There are several models trying to predict the late blight appearance date of
which one of the most well known is SIMCAST. Also Cox regression models have recently been looked into. Is it possible to improve these models?
Moreover what financial impact will it have for farmers?

First an empirical investigation of the data was conducted for a general
understanding of the subject. Then it was analyzed using, linear regression,
Cox regression, Elastic net and cross-validation. To fill in missing data imputation was used.

The best performing model was obtained through Cox regression using
SIMCAST and planting day as covariates. It had approximately
the same failure ratio as foliage prediction which is currently used, but had
23% more accurate predictions and lower standard deviation. SIMCAST
was principally out-performed by all other models. Using elastic net it was
possible to obtain models which was approximately as good as the best
model. Using the best model savings around 422 SEK/ha can be made from
reduced fungicides alone.

The newly obtained models has potential to increase the profit of potato
cultivation. Also by these the accuracy of prediction would contribute to
lower environmental impact. Notable is that the data used is not optimal
for the experiment as some had to be estimated etc. hence before relying
on these models field experiments should be considered.},
  author       = {Bengtsson, Lukas},
  language     = {eng},
  note         = {Student Paper},
  series       = {Bachelor's Theses in Mathematical Sciences},
  title        = {Late Blight Prediction and Analysis},
  year         = {2017},
}