Estimation of wheat planting date using machine learning algorithms based on available climate data

Gümüşçü, Abdülkadir; Tenekeci, Mehmet Emin; Bilgili, Ali Volkan

doi:10.1016/j.suscom.2019.01.010

Cited by 26 publications

(8 citation statements)

References 13 publications

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“…For instance, a study proposed an ML-based integrated approach to forecast the seasonal rainfall for assessing the climatic conditions in Australia (Feng et al 2020). Gümüşçü et al 2019 estimated the wheat crop planting date using ML algorithms based on the available climatic data. The data was collected and processed using genetic algorithms, where farmers could analyze the conditions and take decisions.…”

Section: Digitalizationmentioning

confidence: 99%

Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming

Sridhar

Balakrishnan

Jacob

et al. 2022

Environ Sci Pollut Res

134

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The rise and spread of the coronavirus pandemic (COVID-19) has created an imbalance in all sectors worldwide, massively disrupting the global economy. Social distancing, quarantine regulations, and strict travel restrictions have led to a major reduction in the workforce and loss of jobs across all industrial sectors. One of the sectors completely exposed was the agriculture and food sector. The initiation of a nationwide lockdown by the government resulted in the shutdown of industries globally impacting the overall supply chain from farmer to consumer. The need of the hour is to propose effective solutions which can serve the dual purpose of market growth as well as customer satisfaction. This paper reviews the impact of COVID-19 on the agro-food system and its economy stressing critical factors like food production, demand, price hikes, security, and supply chain resilience. To conserve natural resources and meet the sustainable development goals (SDG), importance has been given to adopting sustainable agricultural practices with a prime focus on techniques like urban agriculture, crop rotation, hydroponics, and family farming. Possible advancements like the use of digital tools, mainly artificial intelligence, machine learning, deep learning, and block-chain technology, in the agro-food sector have been discussed as they could be a promising tool to develop a self-reliant society. This work would be a perfect platform to understand the growing impact of the pandemic as well as supporting cost-effective solutions for a green ecosystem.

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Section: Digitalizationmentioning

confidence: 99%

Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming

Sridhar

Balakrishnan

Jacob

et al. 2022

Environ Sci Pollut Res

134

View full text Add to dashboard Cite

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“…Bu çalışmada sarmal modelli öznitelik seçme yöntemi olan genetik algoritma ile öznitelik seçme yöntemi kullanılmıştır. Genetik algoritma birçok alanda kullanılan meta sezgisel bir algoritmadır [44][45][46]. Genetik algoritma ile öznitelik seçme işleminde oluşturulan nesillerin nasıl anlamlandırıldığı Şekil 4'de gösterilmiştir.…”

Section: öZnitelik Seçme Yöntemleriunclassified

Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi

Gümüşçü

2019

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

Self Cite

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Öz: Yürüme, canlıların bilinen en eski aktivitelerinden biridir. Konum değiştirmek amacı ile insanların kas ve kemik sistemlerinin koordineli bir şekilde hareket etmesine yürüme denir. Yürüme biyometrik bir ölçüt olarak kabul edilmektedir. Bu yüzden yürüyüş analizi ile kişi tanıma, yaş belirleme, cinsiyet belirleme, nörolojik ve ortopedik hastalık tespiti gibi çalışmalar yapılabilmektedir. Bu çalışmada da giyilebilir yürüyüş analizi sensörü ile kişilerin sınıflandırılmasında sarmal modelli öznitelik seçme yöntemleri kullanılarak daha başarılı sınıflandırma başarı parametrelerinin elde edilmesi amaçlanmıştır. 7'si bayan 9'u bay olmak üzere toplam 16 farklı gönüllü kişinin yürüyüş parametreleri hesaplanarak yürüyüş veri seti oluşturulmuştur. Her gönüllüden 3 kez yürümeleri istenmiş olup toplam 48 yürüyüş ele alınmıştır. Kişi sınıflandırma başarı parametreleri k-en yakın komşuluk yöntemi kullanılarak hesaplanmış olup birini dışarıda bırak çapraz doğrulama yöntemi ile doğrulanmıştır. Sınıflandırma sonuçları ele alındığında 0,979 doğruluk oranı elde edilmiştir. Sonuçlar sınıflandırma başarı parametreleri ve sınıflandırma işlem süresi bakımından incelenmiş olup elde edilen sonuçlar çalışmanın sınıflandırma başarı parametreleri ve sınıflandırma işlem süresi bakımından ciddi oranda iyileştirmeler sağladığı gözlemlenmiştir.Anahtar kelimeler: Yürüyüş analizi, sarmal modelli öznitelik seçme, sınıflandırma, kişi tanıma.Abstract: Gait is one of the oldest known activity. It is called gait in the coordinated manner of muscle and bone systems of people with the purpose of changing position. Gait is considered a biometric criterion. Therefore, studies such as human identification, age determination, gender identification, detection of neurological and orthopedic diseases can be done by gait analysis. In this study, it is aimed to obtain more successful classification success parameters by using wrapper model feature selection methods in the human classification with wearable gait analysis sensor. Gait data set was created by calculating the gait parameters of a total of 16 different volunteers including 7 female and 9 male. For each volunteer, 3 different gait parameters were calculated and a total of 48 gait were discussed. Human classification success parameters were calculated by using k-nearest neighborhood method and the results was verified by leave one out cross-validation method. When the classification results are considered, 0.979 accuracy rate was obtained. The results were examined in terms of the classification success parameters and the processing time, the results obtained showed significant improvements in the classification success parameters and the classification process time.

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“…Downpour, storm, rising temperatures, sea level, and retreating glaciers are considered as the main headlines among the indicators of climate change [ 1 – 6 ]. Thanks to the popularity of Twitter and easily accessible Application Program Interface (API) [ 7 – 9 ], tweets can be stored by topics related to hashtags.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

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As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

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Estimation of wheat planting date using machine learning algorithms based on available climate data

Cited by 26 publications

References 13 publications

Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming

Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming

Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

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