Machine Learning Regression Model for Predicting Honey Harvests

Campbell, Tristan; Dixon, Kingsley W.; Dods, Ken; Fearns, Peter; Handcock, R.N.

doi:10.3390/agriculture10040118

Cited by 14 publications

(3 citation statements)

References 20 publications

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“…Machine learning regression model for predicting honey harvests [10] Gradient boosted regression Predict the honey yield per hive with a mean average error (MAE) of 10.85 kg and classify the harvest into the correct quality category with 92% accuracy.…”

Section: Random Tree Regression Algorithmmentioning

confidence: 99%

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A review on internet of things-based stingless bee's honey production with image detection framework

Rohafauzi,

Kassim,

Ja’afar

et al. 2024

IJECE

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Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.

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Section: Random Tree Regression Algorithmmentioning

confidence: 99%

“…The most common effects of extracting less honey are colony loss and an underproductive hive. This has an impact on the beekeeper's capacity to maintain hive health and honey production [10]. Both concerns are exacerbated by the low quality of the stingless bee habitat surrounding the farm.…”

mentioning

confidence: 99%

A review on internet of things-based stingless bee's honey production with image detection framework

Rohafauzi,

Kassim,

Ja’afar

et al. 2024

IJECE

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“…Several machine learning approaches have become popular for achieving superior and precision agriculture [85,86]. The following sub-section discusses certain machine learning approaches that have been deployed for achieving enhanced agricultural benefits.…”

Section: Considered Machine Learning Approaches For Agriculturementioning

confidence: 99%

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Sivakumar

Prabadevi²,

Velvizhi³

et al. 2022

IoT Applications Computing

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Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things.

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Spatial and Temporal Validation of In-Situ and Satellite Weather Data for the South West Agricultural Region of Australia

Campbell

Fearns

2022

J Meteorol Res

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Machine Learning Regression Model for Predicting Honey Harvests

Cited by 14 publications

References 20 publications

A review on internet of things-based stingless bee's honey production with image detection framework

A review on internet of things-based stingless bee's honey production with image detection framework

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Spatial and Temporal Validation of In-Situ and Satellite Weather Data for the South West Agricultural Region of Australia

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