Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector

Kumar, Yogesh; Spandana, V.; Vaishnavi, V.; Neha, Koti; Devi, V.G.R.R.

doi:10.1109/icces48766.2020.9137868

Cited by 125 publications

(59 citation statements)

References 3 publications

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“…The term "Precision" means the proximity of two or more dimensions to each other. as the ratio of true positive to true positive and false sum and is illustrated in the following section (15). 100 Pr × + = FP TP TP ecision…”

Section: Precisionmentioning

confidence: 99%

“…Though crop models were originally designed for homogenized field views, they still be utilized in a spread of domains, love farm, region, country, continent, and world. In addition, the piecemeal application of crop models is employed for the aim of modeling crop development and international policy problems within which agriculture plays a very important role [15] and lots of crop models give helpful techniques for predicting future impacts on crop yields effectively. several crop models provide effective techniques for effectively predicting future impact on crop yields.…”

Section: Introductionmentioning

confidence: 99%

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Soil Forecasting and Classification Using JSO and Intelligent Technique With Big Data on Crop Yield

Nithishkumar¹,

Surya²,

Jebaman³

et al. 2022

Proceedings of the International Conference on Intelligent Technologies in Security and Privacy for Wireless Communication, ITS

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Accurate and rapid spatial classification of soil types and predicted production based on large spatial data has proven to be important factors for realistic purposes. In this regard, spatially clear information about the type of crop can be used constructively to assess the area for a variety of monitoring and decision-making applications, such as crop insurance, land leasing and supplies. Supply chain and financial market forecasts. The main impetus behind the current research is the effective description of the modified support vector machine (MSVM) for efficient classification of soil types. The forecast of the harvest and the expected yield depend entirely on the type of soil. In this paper, it is very important for an effective management of the company to have an adequate production forecast based on the combination of many factors that have a corresponding effect. The document performs three main functions, for example: Significant data reduction, soil classification and plant composition, including production forecasts. The harvest, in fact, varies from farm to farm depending on the date of planting, the variety, the soil and the organization of the harvest. Therefore, the category of soil to be used must be determined effectively. The document shows the big data inserted. The category of soil is determined by the method of shrinking the paper. Kernel principle component analysis (KPCA) in turn removes the maps. Incidentally, map reduction involves two basic processes, such as the cartograph and the reducer. The geographer and therefore the reducer whereas the soil class is decided on the mapping side, the acquisition method is performed on the transmission side.. In addition, the innovative technology takes into account the composition and prediction of crops using the best replicas for Optimal Recurrent Neural Networks (ORNN) and the Jellyfish Search optimization algorithm (JSO). The document proposes a forecast of cultivation and production for the next few years.

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Section: Precisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil Forecasting and Classification Using JSO and Intelligent Technique With Big Data on Crop Yield

Nithishkumar¹,

Surya²,

Jebaman³

et al. 2022

Proceedings of the International Conference on Intelligent Technologies in Security and Privacy for Wireless Communication, ITS

View full text Add to dashboard Cite

show abstract

“…To begin the prediction process, users are encouraged to input parameters such as their location and temperature this application will take it automatically to start the process of prediction. Y. J. N. Kumar et al [24], implemented prediction system on crop production from the collecting of past data. Crop yield is estimated using data mining techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of these unnatural methods is used to prevent losses. However, when the farmers of these crops have reliable information on the crop production then the loss of production is reduced [18,19]. The information is based on previous weather, temperature, season, location, and a variety of other factors [20].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Ismael

Abdulazeez

Hasan

2021

QAJ

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The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.

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“…The application of several data mining methods to improve accuracy rate was proposed [9]. Variables such as ph, humidity, rainfall, temperature, and so on are included in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Review of Machine learning models for Crop Yield Prediction

Aravind¹

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Agriculture is not only a necessary component of human life, but it is also one of India's most important sources of employment. Agriculture employs more than half of our country's population. It is the economic backbone of our country. Farmers can benefit from early diagnosis and control of issues in order to increase crop productivity. Crop yield prediction is an essential scientific area that aids in food security. Based on many parameters, a machine learning model will grasp the pattern of the crop and yield and estimate the yield of the region in which the farmer would crop. The machine learning algorithm can then be distributed after implementation with a web-based visual application that is convenient to use. The results obtained will be granted access to the farmers. This study investigates various machine learning models that are employed in predicting the crop yield.

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Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector

Cited by 125 publications

References 3 publications

Soil Forecasting and Classification Using JSO and Intelligent Technique With Big Data on Crop Yield

Soil Forecasting and Classification Using JSO and Intelligent Technique With Big Data on Crop Yield

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Review of Machine learning models for Crop Yield Prediction

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