Crop Prediction Using Feature Selection and Machine Learning

S. Md Riyaz Naik, Shaik Mohammad Zaheer, Lingala Madhana Damodhar,

Shaik Shahid and Edula Shiva Praneeth Reddy

Department of Computer Science and Engineering, Santhiram Engineering College, Nandyal, Andhra Pradesh, India

Keywords: Crop Yield Prediction, Machine Learning, Stacked Regression, Lasso Feature Selection, Kernel Ridge

Regression, Elastic Net.

Abstract: In many developing countries, farming isn’t just about filling plates it’s the pulse of economies, supporting

millions of livelihoods. Yet farmers and leaders face a relentless struggle: harvests that swing wildly due to

erratic weather, exhausted soils, and shrinking resources like water and fertile land. This uncertainty ripples

through communities, threatening food security and economic survival. By blending three advanced

algorithms Lasso Regression, Kernel Ridge Regression, and Elastic Net the research builds a "stacked" model

designed to predict crop yields with precision. But here’s the game-changer: instead of drowning in endless

data, the model focuses only on the most critical factors like rainfall, temperature, and pesticide use through

a process called feature selection.

1 INTRODUCTION

Agriculture isn’t just about planting seeds it’s the

backbone of economies, feeding billions and

employing millions worldwide. But predicting crop

yields has always been a high-stakes guessing game.

Traditional methods, like relying on past harvest data

or expert opinions, often miss the mark. Why?

Because nature doesn’t follow a script. Climate shifts,

soil changes, and unpredictable weather throw

curveballs that old-school approaches can’t catch.

Enter machine learning, the game-changer.

Imagine a tool that sifts through mountains of data

rainfall patterns, pesticide use, temperature swings

and spots hidden trends even seasoned experts might

miss.

By stacking these techniques, the model becomes

a precision tool. But here’s the kicker: pairing them

with feature selection a process that trims the fat from

datasets supercharges efficiency. Less clutter means

faster computations and sharper predictions.

2 LITERATURE REVIEW

Imagine a world where farmers can predict harvests

with near-surgical precision no crystal ball needed,

just data. That’s the promise machine learning (ML)

has brought to agriculture, revolutionizing how we

forecast crop yields. Over the past decade, researchers

have tested everything from basic algorithms to

cutting-edge AI, and the results are reshaping the field

literally. Early pioneers like Veenadhari et al. (2011)

cracked open the potential of ML by linking climate

factors like rainfall and soil health to soybean yields

using Decision Trees. But the real breakthrough came

with Random Forest models. Shekoofa et al. (2014)

showed these "forests" of decision trees could

outsmart traditional stats in predicting maize yields,

thanks to their knack for spotting patterns in messy,

real-world data. By 2016, sugarcane farmers saw a

15% accuracy boost using Random Forest, proving

ML wasn’t just a lab experiment it worked in the field.

As datasets grew, so did ambition. Researchers

like Gomez et al. (2019) tapped into satellite

imagery and deep learning to predict potato yields,

turning pixels into actionable insights. Meanwhile,

Pandith et al. (2020) pitted K-Nearest Neighbors

(KNN) and Artificial Neural Networks

(ANN) against simpler models for mustard crops.

Spoiler: ANN and KNN won, showing complex

relationships (think soil pH + monsoon timing) matter

more than single factors. Why settle for one algorithm

when you can mix and match? Mishra et al. (2016)

fused techniques like boosting and bagging into

hybrid models, which adapted better to unpredictable

variables (looking at you, climate change). Even older

478

Naik, S. M. R., Zaheer, S. M., Damodhar, L. M., Shahid, S. and Reddy, E. S. P.

Crop Prediction Using Feature Selection and Machine Learning.

DOI: 10.5220/0013915000004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

478-481

ISBN: 978-989-758-777-1

methods like Support Vector Machines (SVM) got a

reality check Mupangwa et al. (2020) found them

lagging behind KNN in maize predictions, proving

not all algorithms age gracefully. But here’s the catch:

more data isn’t always better. Redundant features

(like outdated pesticide records or redundant location

data) can muddy predictions. That’s where this study

steps in. Building on past breakthroughs, the team

combines Lasso-based feature selection-a smart way

to ditch irrelevant variables with stacked

regression (think: layering Lasso, Kernel Ridge, and

Elastic Net). The result? A leaner, meaner model

that’s faster, sharper, and ready for real-world chaos.

Overall, the literature suggests that machine learning

models, particularly ensemble methods and deep

learning techniques, have significantly improved crop

yield prediction. Expanding on previous research, this

study integrates Lasso-based feature selection with

stacked regression techniques, presenting a refined

methodology for agricultural forecasting.

3 METHODOLOGY

3.1 Architecture

Figure 1: Architecture.

3.2 Dataset Information

To predict crop yields accurately, you need more than

guesswork you need data that tells the whole story.

This study’s dataset acts like a farming diary,

capturing seven critical factors from fields across

diverse regions. Figure 1 show the Architecture.

• Crop Type: From drought-resistant millet to

water-hungry rice, the dataset categorizes

crops to reflect their unique needs.

• Geographical Location: Where a crop grows

matters soil in the Punjab isn’t the same as soil

in the Andes, and neither are local pests or

microclimates.

• Yea r: Harvests from 2010 aren’t the same as

2020. This timestamp helps track trends like

shifting rainfall patterns or evolving farming

tech.

• Average Rainfall (mm/year): Too little?

Drought. Too much? Floods. Rainfall data ties

directly to a crop’s survival or collapse.

• Pesticides (tonnes): A double-edged sword.

This metric reveals how chemical use impacts

both yield and long-term soil health.

• Temperature (°C): A 2°C spike can make or

break a wheat harvest. This variable tracks the

silent role of heatwaves and cold snaps.

• Yield (Quintal/Hectare): The bottom line.

One quintal = 100 kg, so this measures how

much food each hectare of land produces.

3.3 Algorithms Building

To predict crop yields, you need more than one tool

in the toolbox you need a team of algorithms working

together. This study combines three machine learning

techniques, each with a unique superpower:

• Lasso Regression: Think of this as a data

detective. It sifts through variables like

rainfall, pesticide use, and temperature,

tossing out the "red herrings" (irrelevant

features) that distract from the real clues.

This keeps the model focused and efficient.

• Kernel Ridge Regression: Some

relationships in farming aren’t

straightforward like how a heatwave during

flowering impacts yields more than one

during sowing. This algorithm decodes these

hidden, nonlinear patterns, turning chaos

into clarity.

• Elastic Net: The peacemaker. It blends

Lasso’s precision (feature selection) with

Ridge Regression’s stability (preventing

overfitting), ensuring the model doesn’t get

too rigid or too loose.

3.4 Performance Metrics

Predictions mean little without proof. Here’s how the

models were graded:

• Mean Absolute Error (MAE): The average

"oops" factor how far off the predictions are

from reality. Lower = better.

• Root Mean Squared Error (RMSE):

Punishes big mistakes more harshly. A

Crop Prediction Using Feature Selection and Machine Learning

479

heatwave misprediction hurts your score

worse than a small rainfall error.

• R-squared (R²): The "confidence meter."

Scores range from 0 to 1, where 1 means the

model explains all variations in yield (e.g.,

0.90 = 90% accuracy).

• Computational Time: Speed matters. A

model that takes hours to run isn’t practical for

farmers needing real-time advice.

4 RESULTS AND ANALYSIS

4.1 Feature Selection Impact

Imagine trying to listen to a radio station with too

much static it’s hard to catch the actual music.

Similarly, when a machine learning model is

fed all available data including irrelevant details like

outdated pesticide records or redundant geographic

stats it gets overwhelmed by "noise." This noise

distracts the model, leading to shaky predictions.

Enter Lasso-based feature selection, the tool that

acts like a precision filter. It sifts through the data,

identifying and keeping only the most impactful

variables like rainfall, temperature, and soil quality

while tossing out the clutter. This isn’t just about

tidying up; it’s about sharpening the model’s focus.

4.2 Evaluation of Model Performance

Table 1 provides a detailed comparison of various

models in terms of accuracy and computational

efficiency.

Table 1: Evaluation of Model Performance.

Model MAE RMSE

R²

Score

Time

(s)

Lasso 2.45 3.12 0.87 0.32

Kernel

Ridge

2.23 3.01 0.89 0.41

Elastic

Net

2.28 2.98 0.90 0.36

4.3 Impact of Excluding Feature

Selection

Initially, the models were trained

using every available data point geography, pesticide

use, temperature, and more. While they managed

decent accuracy, there was a catch: the models

became too familiar with the training data, almost

memorizing it. This over-attachment, known

as overfitting, happened because the algorithms were

drowning in repetitive or irrelevant details like

analysing every raindrop in a monsoon when only

seasonal totals matter.

Figure 2 show the performance

without Lasso.

Here’s how the models performed without pruning

the data:

• Lasso Regression: R² score of 0.82 (82%

accuracy).

• Kernel Ridge Regression: 0.84.

• Elastic Net: 0.85.

Figure 2: Performance Without Lasso.

4.4 With Lasso-Based Feature

Selection

When models are buried under irrelevant data, even

the smartest algorithms stumble. Enter Lasso feature

selection a method that works like a skilled editor,

trimming away redundant variables (think outdated

pesticide stats or duplicate location tags) to spotlight

only the critical factors: rainfall, temperature, and soil

health.

Figure 3 show the performance with Lasso.

The impact was clear:

• Lasso Regression saw its accuracy (R²) leap

from 0.82 to 0.88.

• Kernel Ridge Regression jumped to 0.90,

mastering hidden climate-crop relationships.

• Elastic Net hit 0.91, balancing precision

and adaptability.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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Figure 3: Performance with Lasso.

5 CONCLUSIONS

This research tackled a critical question: How can we

predict crop yields more accurately to help farmers

and policymakers make informed decisions? The

answer lies in blending advanced machine learning

techniques while cutting through data clutter.

By strategically combining Lasso

Regression, Kernel Ridge Regression, and Elastic

Net three powerful algorithms the study created a

"dream team" model. Here’s how it works:

• Lasso acts like a data editor, removing noise

(like outdated pesticide records or redundant

location stats) to focus on what truly matters:

rainfall, temperature, and soil health.

• Kernel Ridge deciphers complex, non-linear

relationships (e.g., how a heatwave during

flowering impacts yields more than one at

harvest).

• Elastic Net balances precision and flexibility,

ensuring the model doesn’t overcomplicate

itself.

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