A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh

Islam, Tanjila; Chisty, Tanjir Alam; Chakrabarty, Amitabha

doi:10.1109/r10-htc.2018.8629828

Cited by 48 publications

(17 citation statements)

References 7 publications

(4 reference statements)

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“…ey were successful in predicting paddy harvest to Bangladesh with an error threshold. A similar study with more variables was carried out by Islam et al [41]. ey have considered many variables including maximum and minimum temperature, average rainfall, humidity, climate, weather, types of land, types of chemical fertilizer, types of soil, soil structure, soil composition, soil moisture, soil consistency, soil reaction, and soil texture; however, instead of particularly on paddy, they have analyzed total crop yield, but including paddy too.…”

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Amaratunga

Wickramasinghe

Perera

et al. 2020

Mathematical Problems in Engineering

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Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

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Section: Resultsmentioning

confidence: 99%

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Amaratunga

Wickramasinghe

Perera

et al. 2020

Mathematical Problems in Engineering

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“…Crops: Corn, Wheat, Soy, Barl Features: the land available t required for each crop, the co [45] This [47] This paper presents an intelligent system, called Agro-Consultant, which assists farmers in making decisions about which crop to grow.…”

Section: Features: Cropmentioning

confidence: 99%

“…WCs 20 [32,33,35,36,37,40,41,42,39,50,54,57,58,64,71,43,60,67,44,45] SP 19 [32,35,36,37,38,40,41,42,39,50,57,69,68,71,43,65,66,60,45] Geography 17 [35,33,36,37,38,40,41,42,39,50,54,69,71,65,70,67,…”

Section: Feature Class Number Of Studies Referencesmentioning

confidence: 99%

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

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Crop selection (CS) is one of the most critical elements that affects the final yield directly. As a result, selecting an appropriate crop is always a critical decision that a farmer must make, considering environmental factors. Choosing an appropriate crop for a given farm is a difficult decision including a plethora of variables that influence the final yield. Experts are frequently consulted to assist farmers with CS; but, as this alternative is time consuming and expensive, it is not available to many farms. The use of recommender systems (RSs) in agricultural management has recently brought some captivating and promising results. We propose a systematic literature review (SLR) in this article to find and provide the most relevant and high-quality publications ad- dressing the crop recommendation (CR) question. The core concept of this SLR is inspired from the guidelines of PRISMA 2020.The different CR approaches are discussed, as well as all the most important input features for recommendation, which are determined and classified. We also identified some of the biggest hurdles to using CR in agriculture. Besides, we made an inventory of the most used techniques for CR. Further, we made an inventory of evaluation criteria and evaluation approaches.

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“…Criterions favor in this research includes: precipitation, less temperature, moderate temperature, high temperature and reference crop evapotranspiration. In [6], authors used ANN to crop harvest estimation. This research, back propagation method was used to train DNN with three invisible layers to calculate overall cost of the output.…”

Section: Literature Reviewmentioning

confidence: 99%

ML Methods for Crop Yield Prediction and Estimation: An Exploration

Alagurajan¹,

Vijayakumaran²

2020

IJEAT

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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.

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A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh

Cited by 48 publications

References 7 publications

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

ML Methods for Crop Yield Prediction and Estimation: An Exploration

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