# A Machine Learning Approach for Crop Yield and Disease Prediction
Integrating Soil Nutrition and Weather Factors

Forkan Uddin Ahmed  Department of CSE
Chittagong University of
Engineering & Technology
Chattogram, Bangladesh
<EMAIL_ADDRESS>Annesha Das  Department of CSE
Chittagong University of
Engineering & Technology
Chattogram, Bangladesh
<EMAIL_ADDRESS>Md. Zubair  Department of CSE
Chittagong University of
Engineering & Technology
Chattogram, Bangladesh
<EMAIL_ADDRESS>
###### Abstract

The development of an intelligent agricultural decision-supporting system for
crop selection and disease forecasting in Bangladesh is the main objective of
this work. The economy of the nation depends heavily on agriculture. However,
choosing crops with better production rates and efficiently controlling crop
disease are obstacles that farmers have to face. These issues are addressed in
this research by utilizing machine learning methods and real-world datasets.
The recommended approach uses a variety of datasets on the production of
crops, soil conditions, agro-meteorological regions, crop disease, and
meteorological factors. These datasets offer insightful information on disease
trends, soil nutrition demand of crops, and agricultural production history.
By incorporating this knowledge, the model first recommends the list of
primarily selected crops based on the soil nutrition of a particular user
location. Then the predictions of meteorological variables like temperature,
rainfall, and humidity are made using SARIMAX models. These weather
predictions are then used to forecast the possibilities of diseases for the
primary crops list by utilizing the support vector classifier. Finally, the
developed model makes use of the decision tree regression model to forecast
crop yield and provides a final crop list along with associated possible
disease forecast. Utilizing the outcome of the model, farmers may choose the
best productive crops as well as prevent crop diseases and reduce output
losses by taking preventive actions. Consequently, planning and decision-
making processes are supported and farmers can predict possible crop yields.
Overall, by offering a detailed decision support system for crop selection and
disease prediction, this work can play a vital role in advancing agricultural
practices in Bangladesh.

###### Index Terms:

Agriculture, Crop suggesting model, SARIMAX, Crop production, Crop disease,
Haversine formula, Machine learning.

##  I Introduction

Agriculture remains a central lifeline of Bangladesh’s economy, contributing
to 12% of the nation’s GDP and employing 45% of its workforce [1].
Nevertheless, increased population pressure and various challenges are testing
the sector’s capabilities. Key problems include limited knowledge of
appropriate crop selection considering soil nutrition, weather forecasting
limitations, and vulnerability to pests and diseases. Modernizing agriculture
is essential, and the implementation of machine learning and artificial
intelligence knowledge in the age of the fourth industrial revolution can
instigate such transformation.

Crops cultivated in Bangladesh are influenced by numerous factors including
soil nutrients, weather, and disease risks, all varying across the country’s
regions. Recognizing these regulators is crucial as not all crops are suitable
for all areas. Soil testing is particularly important for understanding soil
composition and nutrient levels, paving the way for better crop selection [2].
Machine learning can streamline this process by analyzing soil states and
suggesting suitable crops accordingly.

Weather forecasting is another crucial determinant of crop production [3]. By
incorporating machine learning and AI into weather prediction, more accurate
forecasts can be generated to support crop selection. Furthermore, machine
learning’s predictive capabilities can help to assess the risk level of crop
diseases based on weather parameters such as temperature, rainfall, humidity
and, thereby influencing crop choices.

Despite significant research in crop suggestions based on various parameters,
no work has yet combined weather forecasts, soil nutrition, and disease
prediction for improved crop productivity in Bangladesh. The primary objective
is to blend these factors for crop recommendation. The main challenge lies in
merging disease prediction with weather parameters to enhance the quality of
suggestions. Data related to disease risks and weather conditions need to be
meticulously organized and categorized for different crops.

The contributions of the proposed work can be summarized as follows:

  * •

A unified framework is created for weather prediction in Bangladesh,
integrating different meteorological variables using the SARIMAX time series
model.

  * •

A dataset of the diseases of different crops based on weather parameters
(temperature and humidity) is developed.

  * •

A user-friendly crop-suggesting model is introduced that aids farmers in
making efficient decisions based on their soil attributes, weather forecasts,
and disease risks, thereby enhancing their crop yield and profitability.

Section II delves into a review of existing literature on this topic. Our
methodology is depicted thoroughly in Section III. A practical assessment of
our model is exhibited in Section IV, and the paper is concluded in Section V.

##  II Related Work

Several researchers have been attempting to use modern approaches in the
agricultural field to improve crop production recently. Hatfield et al. [4]
used the Seasonal Autoregressive Integrated Moving Average model, and for
estimating the crop’s yield, they utilized the random forest regression
technique. However, they did not include the impact of diseases on crop
production. S. Khaki et al. [5] introduced a deep neural network model for
forecasting crop production. They identified the significant influence of
weather factors on crop production compared to genotype. They also employed
the neural network model to predict the weather. Saranya et al. [2] used SVM,
KNN, and logistic regression to predict the best crop according to soil tests
and weather forecasts. Elavarasan et al. [6] applied various machine learning
models, both supervised and unsupervised, to guess crop production and saw
that the expectation-maximization algorithm and the support vector machine
gave finer results than the other algorithms based on various error
measurement approaches. Khattab et al. [7] focused on disease prediction based
on weather parameters. They made use of an IoT-based monitoring system to
forecast early plant disease. Ryan et al. [8] utilised Markov random fields,
which are responsible for the spatial element among neighbouring sites for
herbicide resistance. Afrin et al. [9] focused on crop yield prediction by
analyzing soil properties of 28 sub-districts of Bangladesh. Crop yield
predictions were made by using DBSCAN, PAM, CLARA, K-means, and other data
mining techniques and four different types of linear regression. Parveen et
al. [10] gave a review of machine learning methods for agricultural crop
disease prediction. It discussed the integration of meteorological factors and
historical disease data and stressed the potential of machine learning in
enhancing disease management tactics. Limitations in data availability and
quality were found, particularly in emerging areas. The intricacy of disease
connections and the requirement for precise and timely disease data were also
stressed. An early crop disease prediction method using machine learning was
described by Vijayakumar et al. [11]. It investigated the use of
meteorological variables and physiological information about plants to create
forecasting models. To safeguard crops, the study emphasized the value of
early disease identification. The complexity of disease interactions and the
requirement for real-time data updates were also cited in the article as major
obstacles to creating reliable and accurate disease prediction models. Ahmed
et al. [12] used a Human-Computer Interaction (HCI) oriented method namely
Soft System Methodology (SSM) along with different machine learning models
such as Naive Bayes, j48, Sequential Minimal Optimization, and Multiclass
classifier for predicting crop yields. Aggarwal et al. [13] proposed a machine
learning-based integrated solution for crop recommendation and yield
prediction. It developed thorough models by combining soil properties,
meteorological variables, and historical yield data. The study highlighted the
advantages of including these aspects while making agricultural decisions. The
report discussed the difficulties in gathering data, particularly when it
comes to precise and dependable soil and meteorological data. It also
suggested that models needed to be improved to fit certain agroecological
zones.

##  III Outline of Methodology

This section describes the suggested approach for our work. Bangladesh is
mainly an agricultural country and its economic development vastly depends on
the advancement of the agricultural sector. But this field faces a lot of
difficulties. The cultivable land is reducing over the years when the demand
for production is increasing. climate change and its consequences are also
making a large impact on the production of agricultural products. It was
challenging to identify the most productive crops. We have to follow a
sequential process to get short-listed suggestions for the top-producing crops
and the possible disease vulnerability forecast for each crop on the suggested
list. Making real-world datasets is challenging because crop productivity is
heavily influenced by weather, soil quality, season, and disease possibility.
The methodology contains several steps, depicted in Fig. 1. The steps applied
in the methodology are discussed in the next sections.

###  III-A Dataset Preparation

Bangladesh has 64 districts, 492 sub-districts, and 30 agroecological zones
[12]. We have used seven different datasets for the proposed work. Details
about each of the datasets are presented in the following Table I and
discussed in the following sections.

Figure 1: Overall system architecture  TABLE I: DATASET OVERVIEW Dataset Name |  Attributes |  Data Source
---|---|---
Soil Nutrition |  District, Sub-district, Longitude, Latitude, Agro-ecological zone, pH, Phosphorus, Potassium, Nitrogen |  Bangladesh Soil Institute [14]
Crop Nutrition |  Crops, Potassium, Phosphorus, Nitrogen |  Agriculture Dept. of Bangladesh [15]
Crop Production |  Temperature, Rainfall, pH, Crops, Production |  Agriculture Dept. of Bangladesh
Crop Disease |  Region, Latitude, Longitude, Temperature, Humidity, Crop Diseases |  Bangladesh Agro-Meteorological Dept. [16]
Monthly Avg. Temperature |  Year, Month, Avg. Temperature |  Bangladesh Meteorological Dept. [17]
Monthly Avg. Rainfall |  Year, Month, Avg. Rainfall |  Bangladesh Meteorological Dept.
Monthly Avg. Humidity |  Year, Month, Avg. Humidity |  Bangladesh Meteorological Dept.

####  III-A1 Meteoro-agro Region & Soil Nutrition Dataset

The types of soil nature and the level of different nutrient elements in the
soil of all locations are not similar [18]. That is why, dividing the areas
into different agroecological zones based on soil nutrition level is a must to
predict the production and disease possibilities for different crops in
different regions.

This dataset is a compilation of information related to soil characteristics
and agricultural dependencies in different regions of Bangladesh. It includes
details such as the division, district, upazila (sub-district), latitude, and
longitude of each location.

Additionally, it provides information on the agroecological zone number and
name, meteorological station, the pH level of the soil, and the content of
phosphorus and potassium in the soil.

####  III-A2 Crop Nutrition Dataset

The rate of production of crops depends on the amount of soil nutrition gained
by crops. All crops do not require the same level of nutrition elements.
Hence, we have to keep in mind the soil nutrition facts of a region to suggest
the best productive crops for that region. This dataset contains information
about the nutrient requirements of various crops, specifically regarding
phosphorus and potassium levels. The dataset includes 18 crops along with
their corresponding phosphorus and potassium levels.

####  III-A3 Crop Production Dataset

Crop production depends on different key factors. Weather parameters and soil
quality affect the production of a crop very much. So, we have created a
dataset of the production of crops in ton/hector for different combinations of
weather parameters and soil quality. This dataset provides information on
rainfall, temperature, pH level, crop type, and corresponding production.

####  III-A4 Crop Disease Dataset

Production of crops is greatly reduced by the impact of different crop
diseases [19]. If we want to build a crop-suggesting system for best
production, then we must consider the possible diseases for the cultivated
crop. On the other hand, diseases of crops depend on different weather
parameters [20]. Temperature and humidity are two of the major weather
parameters affecting crop disease possibilities [21]. The dataset provides
specific information about different crops in different regions of Bangladesh,
including the latitude and longitude coordinates, temperature, humidity, and
the corresponding diseases observed under those conditions. The acquired data
were not in a usable format to be used by machine learning algorithms. So, we
have to create the crop disease dataset using the information of the
Bangladesh Agro-meteorological Department. This dataset helps to detect the
possible diseases for any crop in a given location.

####  III-A5 Monthly Average Temperature Dataset

This dataset is composed of information about the average temperature (in ℃)
by month at different meteorological stations in Bangladesh. Almost 50+ years
of information is placed in the dataset which has been used for future
temperature prediction. The predicted temperature has been used to predict the
production of crops and possible crop diseases.

####  III-A6 Monthly Average Rainfall Dataset

This dataset is a collection of information about the average rainfall (in mm)
per month at different meteorological stations in Bangladesh. There are almost
50+ years of information in this dataset for machine learning approach-based
future rainfall prediction. The predicted rainfall along with the predicted
temperature have been used for predicting the production of crops.

####  III-A7 Monthly Average Humidity Dataset

Information about average the humidity (in %) by month of different
meteorological stations in Bangladesh is stated in this dataset. Like the
other two weather parameter datasets, almost 50+ years of information is
available in the dataset for future humidity prediction. The predicted
humidity has been used to predict possible crop diseases.

###  III-B Location Based Soil Nutrition Extraction

At first, the closest sub-district (agro-meteorological zone) to the user
location is determined by applying the Haversine formula [22]. If the latitude
and longitude of two sites on Earth are known, the distance between them can
be determined using the Haversine formula. The Haversine formula is derived
from the Haversine law by using the sides and angles of spherical triangles.
The Haversine formula is as follows:

$a=\sin^{2}\left(\frac{\Delta\phi}{2}\right)+\cos(\phi_{1})\cdot\cos(\phi_{2})\cdot\sin^{2}\left(\frac{\Delta\lambda}{2}\right)$
(1) $c=2\cdot\arctan 2\left(\sqrt{a},\sqrt{1-a}\right)$ (2) $d=R\cdot c$ (3)

where:

  * •

$\Delta\phi$ is the difference in latitude between the user location and the
agricultural zone,

  * •

$\Delta\lambda$ is the difference in longitude between the user location and
the agricultural zone,

  * •

$\phi_{1}$ and $\phi_{2}$ are the latitudes of the user location and the
agricultural zone respectively,

  * •

$R$ is the radius of the Earth,

  * •

$a$ is the square of half the chord length between the user location and the
agricultural zone (also known as the haversine),

  * •

$c$ is the angular distance in radians between the user location and the
agricultural zone,

  * •

$d$ is the distance between the user location and the agricultural zone in the
same units as the Earth’s radius.

Here we estimated the closest possible agricultural zone and meteorological
station for the given user location based on latitude and longitude. It is
necessary for the precision of soil nutrition identification and weather
information accuracy. Then, using the Meteoro-agro Region & Soil Nutrition
Dataset, which contains information on soil nutrition according to the
meteorological zone and agricultural zone, the information about the soil
nutrition level of the nearest sub-district is found.

###  III-C Primary Crops Finding

In this phase, the crop nutrition dataset is utilized to determine whether the
found soil nutrition level satisfies the appropriate crops’ needs or not for a
given user area. A list of the major crops is thus discovered.

###  III-D Weather Parameter Forecast

The suggested model automatically pulls the local temperature, rainfall, and
humidity data from respective datasets when the user location is chosen.
Rainfall, humidity, and temperature fluctuate from month to month.
Furthermore, the time series data is one-dimensional. We have employed the
SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous
Variables) model, which can more accurately capture the seasonal pattern, as
temperature, rainfall, and humidity are seasonal data[23]. SARIMAX(p, d, q)(P,
D, Q, s) can be used to specify the SARIMAX model where p, d, and q represent
the successive order of differencing and moving average terms, respectively,
in autoregressive terms. Additionally, P, D, Q, and s stand for the seasonal
duration, seasonal moving average terms, seasonal order of differencing, and
seasonal autoregressive terms respectively.

The best SARIMAX model parameters were found by using the Akaike Information
Criterion (AIC) measurement. AIC is a statistician’s tool for assessing the
relative merits of several statistical models, especially in the context of
model selection. It offers a trade-off between a model’s goodness of fit and
complexity. In this work, the best SARIMAX model parameters for temperature,
rainfall, and humidity are as follows:

  * •

Temperature: SARIMAX(1, 0, 0) (2, 1, 0, 12)

  * •

Rainfall: SARIMAX(1, 0, 0) (0, 1, 1, 12)

  * •

Humidity: SARIMAX(1, 0, 1) (1, 1, 0, 12)

###  III-E Crop Production Prediction

In previous subsections, we have retrieved information on the primary
cultivable crops, temperature, and rainfall. Our primary prediction model was
developed at this stage. Our suggested model uses the supervised regression
machine learning technique because this is the prediction of a continuous
variable. Applying the Decision Tree Regression (DTR) technique, the
prediction model is trained to forecast the main cultivable crops’ production.

###  III-F Crop Disease Prediction

Crops are vulnerable to diseases which vary based on weather conditions. The
model is given the agricultural location along with its predicted temperature,
humidity, and selected crop list based on crop and soil nutrition. The model
then predicts the possible diseases and total disease count for each of the
primary selected crops. We have used the Support Vector Classifier (SVC) to
predict the possible disease for a list of crops.

##  IV Result and Discussion

This section is focused on the discussion of the outcome of each of the steps
of our work. Results acquired from each step of methodology and comparative
analysis of the prediction models are shown in the following subsections.

###  IV-A Experimental Results

The suggested model was developed using Bangladesh’s geography and put into
practice within the setting of Bangladeshi agriculture. With the datasets we
have gathered, we have assessed the model. The subsections that follow talk
about the experimental results we achieved.

####  IV-A1 Location Based Soil Nutrition Extraction

Our methodology begins by finding the longitude and latitude of the user’s
current position. The closest sub-district is then determined by the model
using the Haversine formula. Consider utilizing the Rangpur Sadar Upazila as
the test location (Longitude: 25.740580 °N, Latitude: 89.261139 °E). The model
selects the closest agricultural zone and retrieves the pertinent information
about soil nutrition as shown in Table II.

####  IV-A2 Primary Crops List

The crop nutrition dataset is used to choose the main cultivable crops based
on the information about soil nutrition from Table II. A crop will be chosen
as the major cultivable crop if its nutritional value satisfies the
nutritional standard for that area. The selected crops are displayed in Table
III.

####  IV-A3 Weather Parameter Forecast

Crop productivity and disease susceptibility are regulated by three key
factors including temperature, rainfall, and humidity. SARIMAX model is used
for forecasting Temperature (in ℃), rainfall (in millimetres), and humidity
(in %) for the given user location which is Rangpur Sadar Upazila. These
weather parameters have been derived from three weather datasets.

TABLE II: EXTRACTED SOIL NUTRITION DATA Parameter | Value
---|---
Agro-Meteoro Zone | Rangpur
Latitude | 25.740580Â°N
Longitude | 89.261139Â°E
pH | 5.6-6.5
Phosphorus | VH
Potassium | M
TABLE III: PRIMARILY SELECTED CROPS Initial Order | Crop Name
---|---
1 | Garlic
2 | Lentil
3 | Papaya
4 | Rice
5 | Soyabean
6 | Sugarcane
7 | Tomato
TABLE IV: EXTRACTED WEATHER DATA Month | Temp.(°C) | Rain.(mm) | Hum.(%)
---|---|---|---
Jan | 15.8 | 0 | 82
Feb | 20.5 | 10 | 75
Mar | 23.7 | 24 | 68
Apr | 26.6 | 94 | 77
May | 27.4 | 232 | 82
Jun | 29 | 289 | 80
Jul | 28.4 | 542 | 83
Aug | 28.4 | 572 | 85
Sep | 28 | 299 | 84
Oct | 27.1 | 116 | 84
Nov | 22.6 | 3 | 78
Dec | 18.4 | 0 | 80

The temperature, rainfall, and humidity statistics of the user location for
2023 are displayed in Table IV.

####  IV-A4 Crop Disease Prediction

By using the temperature and humidity data for user location from Table IV and
primary selected crops list from Table III, the possible diseases for each
crop are suggested in Table V.

####  IV-A5 Crop Production Prediction

The soil nutrition, primary cultivable crops, expected temperature, and
rainfall are all obtained from the aforementioned procedures. In this stage,
the system trains the proposed regression model to forecast the production
amount of the primary selected crops by using the crop production dataset.

####  IV-A6 Final Suggested Crops List

The model then arranges the predicted production for different crops in
decreasing order. The model generates the final list of crops displayed in
Table VI for the stated user location (Rangpur Sadar Upazila).

TABLE V: PREDICTED CROP DISEASES  Initial Order | Crop | Count | Disease
---|---|---|---
1 | Garlic | 0 | -
2 | Lentil | 1 | Foot rot
3 | Papaya | 0 | -
4 | Rice | 0 | -
5 | Soyabean | 1 | Anthracnose
6 | Sugarcane | 1 | Smut
7 | Tomato | 0 | -
TABLE VI: FINAL CROPS LIST  Final Order | Crop | Production (ton/hectare) | Disease
---|---|---|---
1 | Papaya | 134.24 | Not found
2 | Sugarcane | 106.79 | Smut
3 | Tomato | 35.17 | Not found
4 | Garlic | 12.79 | Not found
5 | Soyabean | 11.44 | Anthracnose
6 | Rice | 7.99 | Not found
7 | Lentil | 0.85 | Foot rot

###  IV-B Comparative Analysis of Disease Prediction Models

Different classification models have been applied to find the best-suited
model for predicting the possible diseases of the suggested crops. A few well-
known factors have been considered for this purpose which are stated below:

  * •

Accuracy: A classification model’s overall correctness can be estimated by its
accuracy. It determines the proportion of accurately predicted occurrences to
all of the dataset’s instances.

  * •

Precision: Precision is the measurement of the accuracy of positive
predictions given by the model. It is calculated as the proportion of true
positives to the total number of positive predictions (true positives + false
positives) from the whole dataset.

  * •

Recall: Recall represents the ability of a model to accurately recognize all
relevant instances in the dataset. It can be measured as the ratio of true
positives to the total number of real positives (true positives + false
negatives).

  * •

F1 Score: The harmonic mean of precision and recall can be stated as the F1
score. The trade-off between precision and recall is balanced by this F1 score
and it suggests a single summarizing score about a model’s performance.

Using the crop disease dataset, the Support Vector Classifier (SVC), Random
Forest Classifier (RFC), Gradient Boosting Classifier (GBC), and Logistic
Regression (LoR) model are trained and tested on the evaluation metrics. The
findings of the comparative error analysis are shown in Table VII.

Most often, higher values of accuracy, precision, recall, and F1 score
indicate the best classification model. For this work, SVC provides the best
disease predictions rather than other models.

###  IV-C Comparative Analysis of Production Prediction Models

To forecast the production of the primarily selected crops, different
regression methods have been compared to choose the most compatible model. The
most common method of assessment is to estimate how well the projected value
matches the actual value. The following metrics for evaluation are
investigated for choosing the best model:

  * •

Mean Squared Error (MSE): The mean square difference between predicted and
actual values for regression algorithms is known as MSE.

  * •

Root Mean Squared Error(RMSE): This error type provides the root mean square
difference between the anticipated and real values.

  * •

R-squared: R-squared value shows how appropriate the data is to the fitted
regression line.

TABLE VII: EVALUATION METRICS FOR THE DISEASE PREDICTION MODEL Model | Accuracy | Precision | Recall | F1 Score
---|---|---|---|---
SVC | 0.94 | 0.91 | 0.94 | 0.92
RFC | 0.91 | 0.89 | 0.91 | 0.90
GBC | 0.90 | 0.89 | 0.90 | 0.90
LoR | 0.56 | 0.59 | 0.56 | 0.57
TABLE VIII: EVALUATION METRICS FOR THE PRODUCTION PREDICTION MODEL Model | MSE | RMSE | R-Squared
---|---|---|---
DTR | 1.06 | 1.03 | 0.997
RFR | 1.19 | 1.09 | 0.996
LR | 42.89 | 6.54 | 0.891
GBR | 4.54 | 2.13 | 0.988

The optimal regression procedure for the final forecast is what we are aiming
for. Using the crop production dataset, the Linear Regression (LR), Decision
Tree Regression (DTR), Gradient Boosting Regression (GBR), and Random Forest
Regression (RFR) models are trained and tested. The findings of the
comparative error analysis are shown in Table VIII.

The lower MSE and RMSE values suggest that the forecasted result and the real
outcome are quite close to each other. The R-squared value, on the other hand,
ranges from 0 to 1. The greater the number is, the more accurately the model
is fitted. DTR is the ideal model for our system, according to the findings.

##  V Conclusion

In this work, we have created a model to recommend the most productive crops
along with probable crop diseases based on the user’s location. Bangladesh
serves as the backdrop for the creation of the whole work. The fast-expanding
population increases food consumption, yet the amount of arable land is
steadily shrinking in this country. Therefore, increasing agricultural output
is now unavoidable. In this work, a model that suggests economically and more
productive crops throughout the year in any place has been developed using
machine learning techniques.

We utilized SARIMAX as the rainfall, humidity, and temperature forecasting
model for the weather forecast. Then, probable disease is predicted based on
temperature and humidity for the primary selected crops. At last, crop
production is determined using a regression model based on soil nutrition data
and forecasted temperature and rainfall data. Compared to other models, the
SVC model shows better outcomes for crop disease prediction and the DTR model
performs better in predicting crop yield. Hopefully, the application of our
strategy to the agriculture sector would increase crop yield and result in
economic growth. We intend to provide Bangladeshi farmers access to this
technology through a mobile platform in the future. Converting this work into
the form of software will be more beneficial and easier for the farmers to
know which crops they should cultivate to maximize their economic profit and
also get prepared for possible crop diseases. Besides, the work can be
extended by using more crop and disease data along with more weather parameter
dependency.

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