Agricultural Crop Recommendations Based on Productivity and

Season

Boya Ashwini, Gundampalle Mamatha, Ampireddygari Durga Bhavani,

Ediga Gayathri and Vemula Rohini

Department of CSE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India

Keywords: Crop Recommendation System, Decision Tree Algorithm, Climate‑Resilient Agriculture, Geographical

Information Systems (GIS), Machine Learning in Agriculture.

Abstract: Agriculture plays a vital role in ensuring global food security and economic stability. However, the increasing

uncertainty of environmental conditions poses significant challenges to farmers, affecting crop yields and

sustainability. Agriculture and machine learning have a significant relationship, as machine learning can be

applied to various aspects of agriculture to improve efficiency, productivity, and sustainability. To address

this issue, our project aims to develop an innovative agricultural crop recommendation system that integrates

farmer specific data and real-time environmental data to provide personalized, climate-resilient suggestions.

The proposed system leverages decision tree algorithms for interpretability and accuracy. Additionally, it

incorporates Geographical Information Systems (GIS) for spatial analysis of soil and weather patterns. By

integrating farmer- specific data, such as resource availability, and risk tolerance, with real-time

environmental data, including weather patterns, soil moisture, and temperature, the system provides farmers

with sustainable, location-specific crop recommendations. The outcomes of this project include improved

crop yields and productivity, reduced risks associated with climate uncertainty, enhanced economic outcomes

and stability, and the adoption of climate-resilient agricultural practices. The proposed system which is based

on Decision tree algorithm has resulted in 17.46% more accurate than the previous recommender system that

is based on the SVM algorithm.

1 INTRODUCTION

1.1 Machine Learning

Machine learning refers to a part of artificial

intelligence that focuses on building systems that can

independently enhance their functionality through

learning- an activity that does not need to be

programmed. The central concept of machine

learning is to allow a computer to take corrective

actions by identifying patterns, drawing inferences,

and resolving issues without relying on detailed input

from a programmer. Machine learning models can

extract information by recognizing associations and

patterns from the past data and experiences. These

insights are progressively used to predict and classify

novel as well as unknown data or even to enhance

decision- making systems. The application of

machine learning is present in many areas such as

natural language processing, computer vision,

recommendation engines, autonomous vehicles,

finance, and healthcare. Machine learning has the

potential to solve difficult problems and foster

creativity in innovative science and technology,

particularly due to the emergence of big data and

powerful computation and machine learning models

and algorithms.

1.2 ML-Based Crop Selection and

Recommendation Systems

An agricultural crop recommendation system is

designed to gather information and provide tailored

crop suggestions to farmers based on the specific

conditions of their farms, historical crop yields, and

current seasonal trends. The goal of this system is to

optimize crop selection, enhance yields, and boost

overall agricultural productivity. By utilizing

machine learning algorithms and Geographic

Information System (GIS) technology, it offers

personalized crop recommendations that consider

Ashwini, B., Mamatha, G., Bhavani, A. D., Gayathri, E. and Rohini, V.

Agricultural Crop Recommendations Based on Productivity and Season.

DOI: 10.5220/0013907200004919

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

13-17

ISBN: 978-989-758-777-1

factors like climate conditions, soil characteristics,

and geographical elements. The applications of this

system are extensive, serving everyone from small-

scale farmers to large agricultural businesses. By

adopting this system, farmers can look forward to

better crop yields, lower costs, and increased profits.

Furthermore, it encourages sustainable farming

practices, which play a vital role in food security and

environmental protection.

1.3 Intelligent Data Analysis

Intelligent Data Analysis plays a vital role in

enhancing agricultural practices, especially regarding

crop recommendations. By examining historical

climate data, soil characteristics, and crop yields,

farmers can make well-informed choices about which

crops to plant, the timing of planting, and the best

management practices to maximize productivity.

Climate and weather patterns are essential in

determining the success of crops. Factors like

temperature, rainfall, and soil moisture levels all

influence crop growth and yields. By looking at

historical temperature data, farmers can pinpoint the

optimal temperature ranges for different crops.

Likewise, analyzing rainfall patterns helps identify

the ideal moisture levels for various crops.

Monitoring soil moisture levels can also guide

recommendations for crops that thrive under specific

moisture conditions.

Soil characteristics, including soil type, pH levels,

and nutrient availability, significantly affect crop

productivity. By categorizing soil types, farmers can

suggest crops that are well-suited for each type.

Analyzing soil pH levels aids in recommending crops

that

can

tolerate

certain

ranges.

Evaluating soil nutrient levels allows farmers to

propose crops that align with nutrient needs.

Crop productivity and seasonality are crucial

factors as well. By analyzing historical crop yield

data, farmers can discover high- yielding crops for

particular seasons. Studying seasonal patterns, such

as planting and harvesting times, helps recommend

crops that fit local seasonal cycles. Additionally, crop

rotation strategies can be proposed to enhance soil

health, minimize pests and diseases, and boost crop

yields.

Regional and local factors, including the regional

climate, market demand, and farmer preferences,

must also be considered. By considering regional

climate patterns, farmers can recommend crops that

are resilient to local conditions. Analyzing local

market demand helps suggest crops that cater to

consumer needs in the area. Incorporating farmer

preferences ensures that the recommendations align

with their goals and practices.

To generate accurate and reliable

recommendations, data from various sources can be

utilized, including government agencies, satellite

imagery, and farmers' feedback.

2 BACKGROUNDS

The advent of machine learning algorithms has

allowed for the creation of more advanced models that

can consider various factors and their interactions.

For example, researchers are now able to combine

data on weather patterns, soil quality, crop varieties,

and pest management techniques to develop more

holistic models. As a result, this has led to improved

accuracy in predictions and enhanced decision-

making for farmers and those involved in agriculture.

2.1 Machine Learning Approaches

• Linear Regression (LR): This is one of the

earliest methods in machine learning that has

been applied to predict crop yields.

• Random Forest (RF): This technique uses an

ensemble of decision trees to enhance the

accuracy of predictions.

• Support Vector Machines (SVM): This

method classifies crop yields into various

categories based on the input features.

In summary, the advent of machine learning

algorithms has been a pivotal moment in crop yield

prediction. These algorithms have not only

advanced over time but have also found applications

in other agricultural domains, including precision

farming and agricultural risk management.

3 CURRENT CHALLENGES IN

CROP RECOMMENDATION

SYSTEM

Current crop recommendation systems encounter

several challenges, such as the necessity for more

precise and tailored suggestions, tackling issues

related to data sparsity and cold starts, and

incorporating various elements like climate

conditions, soil characteristics, and geographical

factors. These constraints can result in less than ideal

crop choices, lower yields, and diminished profits for

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

COMMUNICATION, AND COMPUTING TECHNOLOGIES

farmers.

The current systems depend largely on

conventional approaches, word-of-mouth

communication, and minimal data analysis. They

frequently overlook important issues like climate

change, soil degradation, and water scarcity, resulting

in recommendations that are often inaccurate and

unreliable. Additionally, these systems typically

struggle to incorporate various factors, which can

result in poor crop choices and lower yields.

There is a significant demand for smart crop

recommendation systems that can offer farmers

precise and tailored suggestions. These systems need

to consider various factors, such as climate

conditions, soil characteristics, and geographical

elements, to deliver data-driven insights for the best

crop choices. By utilizing machine learning

algorithms, data analytics, and GIS technology, these

intelligent systems can assist farmers in enhancing

yields, minimizing risks, and boosting profits.

3.1 The Advantages of Intelligent Crop

Recommendation Systems

The benefits of intelligent crop recommendation

systems are extensive.

• These systems assist farmers in choosing the

most suitable crops to cultivate by considering

factors like climate conditions, soil

characteristics, and geographical elements,

which can lead to notable increases in both crop

yields and profits.

• They also help mitigate risks for farmers by

providing insights into the potential challenges

of growing various crops under different

circumstances.

• Furthermore, these systems streamline the

crop selection process, allowing farmers to

save time and energy, enabling them to

concentrate on other important tasks such

as crop management and marketing.

4 CONCEPTUAL FRAMEWORKS

In this study, we introduce a new method that utilizes

the Decision Tree algorithm to identify the best crops

for cultivation based on climate and crop data. Our

approach relies on a detailed dataset that includes

historical climate information, soil characteristics,

and geographical elements to train and assess the

algorithm. Additionally, we recommend the most

appropriate fertilizer for the chosen crop, offering

farmers a comprehensive solution for maximizing

crop yields. To ensure our method's accuracy and

reliability, we assess the performance of the Decision

Tree algorithm and compare it with other machine

learning techniques. This thorough evaluation

process allows us to identify the most effective

algorithm for our crop recommendation system. We

also implement strict measures to maintain the quality

and accuracy of our dataset, which is essential for

training and validating our algorithm.

Our approach incorporates Geographic

Information System (GIS) technology to analyze

spatial data and deliver crop recommendations

tailored to specific locations. This integration allows

our system to consider regional differences in

climate, soil, and other environmental factors,

providing farmers with customized guidance for their

particular area. The user interface of our proposed

system is designed to be user-friendly and accessible

to farmers from various backgrounds. Farmers can

easily enter their location, soil details, and other

pertinent information, receiving crop suggestions that

are specifically suited to their conditions. Our system

is built to continuously learn and adapt to new data,

ensuring that its recommendations stay current with

evolving conditions, technologies, and agricultural

practices. This adaptive feature empowers our system

to offer farmers the most relevant and effective

advice, assisting them in optimizing their agricultural

outcomes.

Block Diagram of Crop Recommendation

System Using Machine Learning Shown in Figure 1.

Figure 1: Block diagram of crop recommendation system

using machine learning.

4.1 Input Data

In our agricultural crop recommendation system, we

consider a wide range of agricultural and

environmental factors that influence crop

productivity. For instance, geographic location,

seasonal data, climate elements such as temperature,

rainfall, and humidity, soil traits like pH and nutrient

levels, altitude, the farmer's risk appetite, resource

availability, and many more factors that impact crop

Agricultural Crop Recommendations Based on Productivity and Season

cultivation and productivity are all included. The

system will be validated against various information

like historical data, weather stations, soil examination

results, and satellite photographs to provide the most

accurate and reliable information.

4.2 Data Pre-Processing

Data pre-processing is an essential step in getting our

input data ready for the machine learning model. This

process includes cleaning the data to handle missing

values, outliers, and inconsistencies. We will use

methods like imputation, outlier detection, and data

normalization or standardization to make sure the

data is properly formatted and free from errors or

anomalies.

4.3 Feature Selection

To enhance the performance of our machine learning

algorithm, we need to convert the dataset into an

appropriate format. This includes identifying the most

relevant features that impact the accuracy of our crop

recommendations. We will utilize various feature,

such as converting class attribute values from numeric

to alphabetic and grouping performance classes into

meaningful ranges.

4.4 Crop Recommendation Using

Decision Tree Algorithm

After we finish pre-processing our dataset and

selecting the relevant features, we will train our

Decision Tree algorithm to provide crop

recommendations. This algorithm will consider

various agricultural and environmental factors to

determine the best crops for a specific location.

4.5 System Testing

To ensure that our agricultural crop recommendation

system operates effectively and achieves its goals, we

will perform various types of testing. This will

encompass unit testing, integration testing, functional

testing, and performance testing. Each testing type

will assess different elements of the system, such as its

individual components, the interactions between

modules, overall functionality, and responsiveness.

4.6 System Implementation

The system implementation phase focuses on putting

our agricultural crop recommendation system into

action for farmers and agricultural experts. This

process includes establishing the necessary hardware

and software infrastructure, creating an intuitive user

interface, integrating the trained machine learning

model, and setting up a database to manage historical

agricultural data and user inputs. To maintain the

security and integrity of the system, we will

implement strong security measures to safeguard user

data and prevent unauthorized access.

5 CONCLUSIONS

Agricultural crop recommendation systems that

utilize the Decision Tree algorithm have the potential

to change how farmers choose and grow their crops.

By offering data- driven insights and tailored

recommendations, these systems can assist farmers in

boosting their yields, reducing risks, enhancing

efficiency, and making more sustainable decisions.

The Decision Tree algorithm is particularly

effective for crop recommendations because it can

manage complex relationships between various

factors, is resilient to outliers and noise in the data,

and can scale to accommodate large datasets.

Furthermore, it can analyze the intricate connections

between different variables and the target crop, which

is crucial for effective recommendations.

Our proposed system, which combines the

Decision Tree algorithm with GIS technology, is

designed for farmers of all sizes, whether in developed

or developing nations. By delivering personalized

crop recommendations based on factors like climate,

soil type, and geographical location, our system

empowers farmers to make informed choices,

enhance crop productivity, and boost overall

profitability.

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Agricultural Crop Recommendations Based on Productivity and Season