Analysis of agricultural crop yield prediction using statistical techniques of machine learning

Pant, Janmejay; Pant, Rajendra; Singh, Manoj Kumar; Singh, Devesh Pratap; Pant, Harsh V.

doi:10.1016/j.matpr.2021.01.948

Cited by 60 publications

(28 citation statements)

References 6 publications

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“…As a result, it can be stated that the most accurate results are obtained with DTR and RFR applications especially for wheat, barley and maize yields. When compared to related literature, it can be stated that successful yield predictions with DTR and RFR methods have also been obtained in the studies of Everingham et al (2016) for sugarcane, Ahmad et al (2018) for maize, Shah et al (2018) for corn, Pant et al (2021) for maize, potatoes, rice and wheat.…”

Section: Evaluating the Model Performancementioning

confidence: 89%

“…For this reason, when adding these determinants into the prediction models, an integrated approach is recommended to be followed by including more than one of these weather condition variables (Xu et al, 2019: 944). Accordingly, numerous studies have focused on the effects of meteorological parameters on crop yield, such as Lobell and Burke (2008), Jeong et al (2016), Trnka et al (2016, Xu et al (2019), Kang et al (2020), Pant et al (2021), Shook et al (2021) and Zarei et al (2021). In addition, use of pesticides can be stated as another factor that has influence on crop growth (Pant et al, 2021: 10923).…”

Section: Introductionmentioning

confidence: 99%

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Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

Burhan

2022

EPF Journal

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En önemli ve hayati insan faaliyetlerden biri olarak tarım, küresel, yerel ve çevresel sorunlara karşı oldukça savunmasızdır. Bu kırılganlık COVID-19 pandemisinin ilk aşamalarında da görülmüştür. Bu bağlamda, söz konusu durumların tarımsal ürünlerin talep ve fiyatlama dinamikleri üzerinde önemli etkilerinin olduğu söylenebilmektedir. Yine de tarımsal faaliyetlerin temel amacı, mahsul verimi ve üretimini iyileştirmek olduğu ifade edilebilir. Türkiye'nin hızla değişen koşullarla başa çıkabilmesi için, 11. Kalkınma Planının da ana hedeflerinden biri olarak belirtilen veriye dayalı tarımsal bilgi sistemlerini geliştirmeye devam etmesi gerekmektedir. Dolayısıyla doğru üretim miktarı tahmini, kritik bir görev olarak öne çıkmaktadır. Bu doğrultuda, 1990-2019 dönemi için meteorolojik parametreler, tarım ilacı kullanımı ve rekolteye dayalı veri setlerini kullanarak, Türkiye'deki dokuz ana mahsulün üretim miktarı tahmininde makine öğrenmesi yöntemlerinin geçerliliğinin değerlendirilmesi, bu çalışmanın temel amacı olarak ifade edilebilir. Eğitim aşamasından sonra tüm modellerle üretim miktarı tahmini yapılmış, elde edilen sonuçlar gerçek değerlerle karşılaştırılmıştır. Sonuçlara göre Karar Ağacı Regresyon (KAR) ve Rastgele Orman Regresyon (ROR) yöntemleriyle, bilhassa buğday, arpa ve mısır için başarılı tahminler alınmış, Destek Vektör Regresyon (DVR) yönteminin ise tutarsız tahminler verdiği görülmüştür.

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Section: Evaluating the Model Performancementioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

Burhan

2022

EPF Journal

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“…The Web of Science database includes 24 research manuscripts that use machine learning algorithms, crop yield prediction, and other related keywords from 2017 to 2021. Among these, 14 research manuscripts ( File S3 ) showed that the most commonly-used machine algorithms were the backpropagation neural network (BPNN), decision tree (DT), Gaussian process regression (GPR), k-nearest neighbor regression (KNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), decision trees, and RF ( Sharifi, 2020 ; Pant et al, 2021 ; Kanwal et al, 2021 ). A total of 75% of these studies identified that the RF model performed better in prediction, combining weather parameters, remote sensing data, and field observation data when completing the construction of the model to predict yield.…”

Section: Discussionmentioning

confidence: 99%

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

Feng¹,

Lin²,

Yu³

et al. 2021

PeerJ

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In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

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“…Further, the genetic algorithm was used to select the discriminate feature vectors from the clustered data, and then the Apriori and frequent pattern growth approaches were used for decision‐making. Pant et al 30 utilized different statistical techniques to predict the yield of four primary crops (maize, wheat, rice, and potatoes) in India. Compared to the existing machine learning techniques: support vector machine (SVM), random forest, and gradient boosting, the decision tree classifier achieved high prediction accuracy in the crop yield prediction.…”

Section: Related Workmentioning

confidence: 99%

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

Dwaram

Madapuri

2022

Concurrency and Computation

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Summary In recent times, crop yield prediction gains more attention among researchers communities to expand food production. In this article, an effort is done for crop yield prediction in Andhra Pradesh state (India), because AP highly contributes to the Indian economy. Especially, the crop yield prediction is done for Western Godavari, Prakasam, Guntur, Srikakulam, Krishna, Vizianagaram, Visakhapatnam, and Nellore districts of Andhra Pradesh (India) by estimating the rainfall based on the attributes: average temperature, minimum temperature, maximum temperature, precipitation, cloud cover, and velocity potential. After collecting the data from the Telangana agricultural report (2017–2018), the collected data are preprocessed utilizing the min‐max normalization technique, which is effective in preserving the relationship among data. The normalized data are classified using long short term memory network with Adam optimizer and Huber loss function. The proposed classifier has adaptive learning rates for dissimilar parameters by identifying the first and second‐moment gradient estimation so it has better computational effectiveness, unlike other classifiers. The proposed classifier predicts each site in the collected dataset into one of the three crops: rice, sugarcane, and onion. Finally, the agriculture experts validate the obtained results of the proposed model.

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Analysis of agricultural crop yield prediction using statistical techniques of machine learning

Cited by 60 publications

References 6 publications

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

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