INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 480

Implementation of Crop Selection by Land Dataset Using Machine

Learning

Gayathri U , Dr.Priya Anand

Vels Institute of Science , Technology and Advanced Studies, India

and its economy largely based upon crop productivity. So we can say that agriculture can be pillar of all business in our country.

Selecting of always crop is very important in the agriculture planning. Many researchers studied guess of yield rate of crop, guess

of weather, soil categorizing and crop classification for agriculture planning using machine learning techniques.

Many changes are required in the agriculture department to improve changes in our Indian economy. We can improve agriculture

by using machine learning system which are applied simply on farming sector. Along with all advances in the machines and

technologies used in farming, functional information about different matters also plays a significant role in it. The concept of this

paper is to implement the crop selection method so that this method helps in solving many agriculture problems. This enhances our

Indian wealth by maximizing the yield rate of crop production. In our project crop is predicted by algorithm namely Recurrent

Neural Network (RNN) as proposed and Random Forest (RF) as existing and its accuracy is calculated and compared with other

algorithms.

Selection. Selection of crops depends upon two task that are favorable and critical conditions. Many researches are carried out to

improve agricultural setting. The aim is to get the biggest yield of crops. Many categorizing systems are also applied to get

maximum yield of crops. Machine learning can be used to better the yield rate of crops. The method of crop Selection is bother to

improve crop production.

The construction of crops may depend on geographical conditions of the region like river ground, hill areas or the depth areas.

Weather conditions like humidity, rainfall, temperature, cloud. Soil type may be clay, sandy, salt or peaty. Soil composition can be

copper, potassium, sulphate, nitrogen, manganese, iron, calcium, ph. value or carbon and different methods of harvesting. Many

parameters are used for different Crops to do separate predictions. These prediction models can be studied by using analyzer. These

predictions are classified as two types. One is traditional statistic method and other is machine teaching techniques. Traditional

method helps in predicting single sample spaces. And machine learning methods helps in predicting different predictions. We need

habit of over fitting to their training set. Random forests generally outperform decision trees, but their accuracy is lower than

gradient boosted trees. However, data characteristics can affect their performance.

Decision trees are a popular method for various machine learning tasks. Tree learning "come[s] closest to meeting the requirements

for serving as an off-the-shelf procedure for data mining", say Hastie et al., "because it is invariant under scaling and various other

transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models. However, they are

seldom accurate".

In particular, trees that are grown very deep tend to learn highly irregular patterns: they overfit their training sets, i.e. have low bias,

but very high variance. Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 481

training set, with the goal of reducing the variance. This comes at the expense of a small increase in the bias and some loss of

interpretability, but generally greatly boosts the performance in the final model.

health card. From these top crops recommended, the farmer will also be able to know the economically beneficial crop where the

model implements price prediction module for the crops.

Also a year round plan will be provided to farmer to maximize his cultivation and earnings. Besides generic information regarding

government schemes or soil testing labs will be provided. The researcher along with these functionalities can access certain datasets

that he wants to explore. The researcher can also study the impact of various algorithms on varies data. An add on ‘what if’ scenario

will help not only the farmer but also there searcher to play around with various factors and see the variations in results and proceed

with decision making accordingly.

Implementation

Implementation of Crop Selection by Land Dataset Using Machine Learning" aims to recommend the most suitable crop for

cultivation based on land and climatic conditions. Initially, a relevant dataset containing features such as soil type, soil pH,

assessed using accuracy score, confusion matrix, and classification report metrics.

Upon achieving satisfactory results, the model is saved for deployment purposes using serialization techniques like joblib. An

optional web-based application can be developed using frameworks like Flask, allowing users to input land parameters and receive

crop recommendations in real time. This project not only streamlines the crop selection process for farmers but also promotes

optimal land utilization based on scientific data analysis. Future enhancements could involve integrating real-time weather data,

fertilizer recommendations, and improving model accuracy with advanced ensemble methods.

II. Conclusion

After going through many surveys and through analysis we can conclude that the use of no of various machine learning algorithms

will not only help farmers to get better results but also increase their revenue which for many is a matter of life and death. Currently

farmers make rough estimations based on their previous experiences and plan accordingly, using ML instead will definitely decrease

Also partial waste issue is a area of study which can result in reduced prices using precise scheduling of users’ job. User scheduling

behaviors and partial usage waste will be brainstormed to find an effective solution.

Reference

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 482