scholarly journals Amalgamation of Machine Learning Algorithms for Crop Yield Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 1320-1324
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Research Work ◽  
Crop Productivity ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Regression Methods ◽  
Agriculture Sector ◽  
The People ◽  
Artificial Neural

Agriculture is India’s prime occupation. In Indian economy agriculture plays a major role by means of providing more employment opportunities for the people. In order to provide food to the huge population of India, agriculture sector needs to maximize its crop productivity. This research work presents an approach which uses different Machine learning (ML) techniques by considering the various parameters of cultivated crop to predict the best yield. Further in this Multiple Linear Regression (MLR) technique and artificial neural networks (ANN) are used to make a brief analysis for the prediction crop yield. With the above idea a new model was created, and from this numerical results were obtained. The accuracy and efficiency of the method has been explored and results from the artificial neural network and regression methods were obtained and compared.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

scholarly journals An interaction regression model for crop yield prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javad Ansarifar ◽  
Lizhi Wang ◽  
Sotirios V. Archontoulis
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Yield Prediction ◽  
Soybean Yield ◽  
Global Food Security ◽  
Management Scenario ◽  
Complex Interactions

AbstractCrop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.

scholarly journals Knn based Crop and Fertilizer Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 1453-1456
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Crop Yield ◽  
Accurate Result ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Prediction Problem ◽  
Environmental Statistics ◽  
Classification And Regression ◽  
Using Data

India has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.

scholarly journals Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm

2021 ◽  
Vol 8 (1) ◽  
pp. 30-35
Author(s):  
Jayalakshmi R ◽  
Savitha Devi M
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Soil Fertility ◽  
Learning Algorithms ◽  
Crop Productivity ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Severe Problem ◽  
Agriculture Sector

Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation’s economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate.

Rice Crop Yield Prediction Using Multi-Level Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4280-4286
Author(s):  
G. L. Anoop ◽  
C. Nandini
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
Multiple Linear Regression ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Rice Crop ◽  
Machine Learning Techniques ◽  
Indian Government ◽  
Regression Methods

Agriculture and allied production contributes to Indian economy and food security of India. Crop yield predictive model will help farmers and agriculture department and organization to take better decisions. In this paper we are proposingmulti-level, machine learning algorithms to predict rice crop yield. Here, data were collected from Indian Government website for 4 districts of Karnataka, i.e., Mysore, Mandya Raichur and Koppal, these data were publically available. In our proposed method initially, we have performed data pre-processing using z-score, normalization and Standardizing residuals on collected data, then multilevel decision tree and multilevel multiple linear regression methods are presented to predict the rice crop yield and evaluated the performance of both. The experimental results shows that the multiple linear regression is accurate than the decision tree technique. This prediction will guide the farmer to make better decision to gain better yield and for their livelihood in particular temperature or climatic scenario.

scholarly journals Experimental Analysis of Machine Learning Algorithms Based on Agricultural Dataset for Improving Crop Yield Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 3246-3251
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Bayes Net ◽  
Learning Techniques ◽  
Production Area ◽  
Primary Research Study

Agriculture is the primary research study area in India as agriculture is the main source of income for various communities. In classification algorithm for agricultural dataset according to production, area, crop and seasons. Here, four classification algorithms are used with the help of WEKA tool. These algorithms are namely the present scenario, there is a call to renovate the enormous agriculture data into diverse technologies and make them accessible to the farmer for improved decision making. The endeavor of this work is to find out the finest Random Tree, J48, Bayes Net and KStar etc. The captured results revealed that Random tree algorithm performed well in terms of error rate and provides slightly better performance than KStar, Bayes Net and J48 classifiers. In this paper, our objective is to apply machine learning techniques to mine constructive information from the agricultural dataset to improve the crop yield prediction for major crops in Nashik district of Maharashtra.

scholarly journals Crop Yield Prediction Using Machine Learning Techniques

2020 ◽  
pp. 312-315
Author(s):  
Ashwini I. Patil ◽  
Ramesh A. Medar ◽  
Vinod Desai
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Model Performance ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
The People ◽  
Learning Techniques ◽  
High Production ◽  
Day By Day

Today Indian economy depends upon agriculture. More than 70% of the people in India have taken it as a main occupation, day by day for a particular crop; the formers are not getting proper yield as well as profit due to environmental conditions like soil quality, weather, heavy rainfall, drought, seed damages, fertilizers, pesticides. The farmers not able to produce high production, so taking the historical agricultural data records we can predict the crop yield using machine learning techniques like Linear regression, comparative analysis are done with decision tree, KNN algorithms, using these to achieve the high accuracy and model performance is computed.

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