Crop Suitability and Fertilizers Recommendation Using Data Mining Techniques

Chougule, Archana; Jha, Vijay Kumar; Mukhopadhyay, Debajyoti

doi:10.1007/978-981-13-0224-4_19

Cited by 38 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Archana et al [35] proposed an ontology-based recommendation system for crop quality and fertilizer use, successfully bridging the gap between the farming community and technological applications. The system predicts a relevant crop, taking into consideration the geographical area and soil type, and offers guidance on appropriate fertilizer use.…”

Section: Survey Of Machine Learning Techniques For Crop Predictionmentioning

confidence: 99%

Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

et al. 2022

View full text Add to dashboard Cite

Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Today, however, rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Consequently, in recent years, machine learning techniques have taken over the task of prediction, and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Thus, optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. Conglomerating every single feature from raw data without checking for their role in the process of making the model will unnecessarily complicate our model. Furthermore, additional features which contribute little to the ML model will increase its time and space complexity and affect the accuracy of the model's output. The results depict that an ensemble technique offers better prediction accuracy than the existing classification technique.

show abstract

Section: Survey Of Machine Learning Techniques For Crop Predictionmentioning

confidence: 99%

Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A number of related studies are discussed in this review. Archana et al (2019) have planned the development of a recommendation system based on the related ontology for crop quality and fertilizer recommendation. It closes the loophole between farmers and technology.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

et al. 2022

View full text Add to dashboard Cite

Agriculture is an essential part of human life. The crop productivity is based on the soil and environmental factors. Different crops are cultivated in different areas. Nowadays, the crop productivity level is affected by the climate change and diseases in the crops. Due to this pest infestation, the crop growth is heavily affected. To overcome this problem, the right fertilizer for a particular crop has to be chosen and fertilizer helps farmers to improve the crop productivity rate. This process can be done by using various machine learning techniques. In this work, various features selection techniques with classifiers used to predict the suitable fertilizer for a crop. The experimental results show that recursive feature elimination along the proposed Heterogeneous Stacked Ensemble classifier gives better prediction rate than other methods.

show abstract

“…Different attributes considered to improve agricultural production include productivity, water conservation, cost, labor, and knowledge. IoT connects all agrarian things to the Internet, providing a supportive hand to agriculture 2,3 . Agriculture is the most imperative division in numerous countries, it output food, raw materials, and business.…”

Section: Introductionmentioning

confidence: 99%

Sensor information‐based crop recommendation system using machine learning for the fertile regions of Maharashtra

Shingade¹,

Mudhalwadkar²

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryAgriculture is the major backbone of India. Therefore, the crop recommendation system is important for the farmers and the country as it reflects the country's economic growth. The crop yield rate is affected due to various parameters such as climatic changes, soil properties, temperature, humidity and so forth. An effective prediction system is required to monitor the field and suggest suitable crops that can provide a maximum yield rate. Therefore, a prediction system is developed in the proposed framework for crop recommendation using the sensor information collected from Maharashtra, India. The dataset has been built with the information collected by 250 sensors located in different Maharashtra places. Initially, the gathered dataset is subjected to preprocessing steps like data cleaning, removing duplicate values, and filling up the missing values. Then, robust and flexible machine learning models like decision tree, random forest, and support vector machine are used, which analyze the preprocessed data and predict the suitable crops that show high yield for a particular area. The implementation of the proposed framework is done using Python. The RF classifier achieved the highest accuracy rate of 95% among the three classifiers.

show abstract

Crop Suitability and Fertilizers Recommendation Using Data Mining Techniques

Cited by 38 publications

References 4 publications

Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Sensor information‐based crop recommendation system using machine learning for the fertile regions of Maharashtra

Contact Info

Product

Resources

About