Crop Yield Prediction Using Machine Learning Algorithm

Ranjani, J; Kalaiselvi, V.K.G.; Sheela, A.; Deepika, D.; Janaki, G.

doi:10.1109/iccct53315.2021.9711853

Cited by 13 publications

(3 citation statements)

References 2 publications

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“…If we just have a small amount of input data, this is Classification and regression issues are resolved using a decision tree that performs two essential functions: firstly, it categorizes the features that are pertinent to each decision, and secondly, it determines the best course of action based on the selected features. The plausible choice is given a probability distribution by the Decision Tree algorithm [25].…”

Section: Methods 1) K-fold Cross Validationmentioning

confidence: 99%

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Sharma,

Dadheech,

Aneja

et al. 2023

IEEE Access

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Agriculture contributes a significant amount to the economy of India due to the dependence on human beings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper's primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decisionsupport tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop's growing season. It can decide which crops to produce and what to do in the crop's growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques -convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model's errors compare to those of the other methods.

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Section: Methods 1) K-fold Cross Validationmentioning

confidence: 99%

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Sharma,

Dadheech,

Aneja

et al. 2023

IEEE Access

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“…In addition, the study areas are different, and the author did not give details on the different threshold values used to classify the attributes as low, medium, and high. This work is in the same direction as Ranjani [39], which used association rule algorithms to set up a farmer recommendation system. This system utilizes information about soil, weather, region, season, and past production to recommend the most profitable crops for cultivation in the appropriate environmental conditions.…”

Section: Plos Onementioning

confidence: 99%

Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm

Houetohossou,

Ratheil Houndji,

Sikirou

et al. 2024

PLoS ONE

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Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into ‘low’, ‘medium’, and ‘high’ attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence ‘high yield’ were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability’s impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

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“…Numerous researchers have leveraged machine learning (ML) techniques for crop selection prediction, as evidenced by a range of studies [12][13][14][15]. In addition, several papers have delved into crop yield estimation through the utilization of ML algorithms [16][17][18][19][20], aiming to enhance accuracy. This paper is centered on conducting a comparative analysis of diverse ML algorithms employed in the context of crop selection.…”

Section: Literature Surveymentioning

confidence: 99%

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Kakade,

Kulkarni,

Dhawale

et al. 2023

Green Intell. Syst. Appl.

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Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

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Crop Yield Prediction Using Machine Learning Algorithm

Cited by 13 publications

References 2 publications

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

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