scholarly journals Review of Crop Yield Prediction using Machine Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 4626-4628
Author(s):  
Kale Jaydeep Narayan
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Soil Types ◽  
Yield Prediction ◽  
Growth Season ◽  
Learning Techniques ◽  
Agricultural Yield ◽  
Decision Making Tool

Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.

scholarly journals A Review on Agriculture Crop Prediction Techniques Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 484-488
Author(s):  
Firdous Hina
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Agricultural Yield ◽  
Post Harvesting ◽  
Prediction Techniques

Abstract: Machine learning is a useful decision-making tool for predicting crop yields, as well as for deciding what crops to plant and what to do during the crop's growth season. To aid agricultural yield prediction studies, a number of machine learning techniques have been used. We employed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features used in crop production prediction research in this investigation This paper provides a comprehensive overview of the most recent machine learning applications in agriculture, with a focus on pre-harvesting, harvesting, and post-harvesting issues The papers have been studied in depth, analysed the methodology and features employed, and made recommendations for future study. Temperature, rainfall, and soil type are the most commonly utilised features, according to our data, while Artificial Neural Networks are the most commonly employed method in these models.

scholarly journals ML Methods for Crop Yield Prediction and Estimation: An Exploration

2020 ◽  
Vol 9 (3) ◽  
pp. 3506-3508
Keyword(s):  
Machine Learning ◽  
Detailed Analysis ◽  
Crop Yield ◽  
Learning Process ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Yield Estimation ◽  
Data Set ◽  
Yield Optimization ◽  
Learning Techniques

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

2021 ◽  
pp. 166-186
Author(s):  
Muzaffer Kanaan ◽  
Rüştü Akay ◽  
Canset Koçer Baykara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Use Of Technology ◽  
Learning Techniques

The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as well as maintaining the quality of the crop against adverse environmental elements (such as rodent or insect infestation, as well as microbial disease agents) is becoming more critical for farming practice worldwide. One of the technology areas that is proving to be most promising in this area is artificial intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an overview of how machine learning techniques can help solve the problem of monitoring crop quality and disease identification. The fundamental principles are illustrated through two different case studies, one involving the use of artificial neural networks for harvested grain condition monitoring and the other concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.

Review on Different Traditional and Machine Learning Techniques for Irrigation Planning for Crop Yield Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 3831-3838
Author(s):  
K. M. Sowmya Shree ◽  
M. N. Veena
Keyword(s):  
Machine Learning ◽  
Crop Yields ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Irrigation Planning ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Productivity Rate ◽  
Major Factors ◽  
Variable Analysis

Agriculture is one of the major factors of Indian economy which involves production of crops. Production crops may be food crops or commercial crops like wheat, maize, grams, rice, millets, cotton etc. The productivity of the crops is administered by its weather conditions. Forecasting the crop yields is a challenging task which needs to be addressed. Several data mining technologies are explored for forecasting the crop yields, yet, solutions are complex and infeasible. This paper presents a review of machine learning techniques for irrigation planning to forecast the crop yields are discussed. Various machine learning methods like prediction, classification, regression, clustering are discussed. This study brings a need for an enhancement in irrigation planning using machine learning techniques. To increase the productivity rate of the crops, variable analysis also play a significant part in defining predictive models. Comparative analysis is done on machine learning techniques and its benefits are explored.

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