Machine learning-based farm risk management: A systematic mapping review

Ghaffarian, Saman; Voort, Mariska van der; Valente, João; Teki̇nerdoğan, Bedir; Mey, Yann de

doi:10.1016/j.compag.2021.106631

Cited by 19 publications

(7 citation statements)

References 153 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A fourth development trend focuses on the design of parametric insurances based on the characterisation of operational risk (OR) for different management scenarios [41]. Within this trend, the first group of studies shows how the ML concepts have been used to model OR [42]. Some researchers have defined fuzzy-inference models for the qualitative description of scenarios for OR management [8] or to determine the inherent risk as a result of implementing different management scenarios when mitigating OR [43].…”

mentioning

confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

View full text Add to dashboard Cite

Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.

show abstract

mentioning

confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Nos últimos anos as metodologias de ML vêm sendo implementadas no campo da ciência, o qual inclui a economia agrícola [22,23]. Os métodos SVR e ANN têm sido utilizados extensivamente em problemas de previsão de séries temporais.…”

Section: Metodologiaunclassified

Análise e previsão do resultado financeiro da produção de soja

Gonçalves Guindani,

Henrique Dal Molin Ribeiro,

Donizetti de Lima

et al. 2023

Anais Do International Conference on Production Research Americas

View full text Add to dashboard Cite

A soja é a principal cultura plantada e exportada, contribuindo de forma expressiva para a balança comercial brasileira. Este estudo tem como objetivo averiguar o comportamento do resultado financeiro da produção da soja. Para atingir este objetivo, métodos de séries temporais (modelo integrado de médias móveis, Naïve e método de Holt-Winters) e modelos de aprendizado de máquina (redes neurais artificiais e regressão por vetores suporte) são utilizados para gerar previsões de curto prazo para a quantidade produzida, custo de produção e preços. O desempenho dos modelos é avaliado por meio da raiz do erro quadrático médio e erro percentual absoluto médio. Esta pesquisa utiliza dados secundários, obtidos em bases de dados governamentais (SEAB/DERAL e CONAB). Diante disso, são usadas séries temporais referentes as variáveis V1-preço unitário (em reais por sacas de 60 kg), V2-custo total unitário de produção (em reais por sacas de 60 kg) e V3-produção média por ha (em sacas de 60 kg). Em relação aos resultados, para previsão de um ano à frente, permitem verificar que para a variável V1 o método SVR apresentou menor MAPE (13,8%), já o método ANN teve o melhor desempenho para previsão da variável V2 com MAPE de 2,5%, já para a variável V3 o modelo ARIMA apresentou menor MAPE, o que aponta que o modelo errou cerca de 10,8% nas previsões. As informações deste artigo são relevantes para o processo de tomada de decisão sobre o que produzir na próxima safra.Palavras chaves: Soja, Aprendizado de máquina, Previsão de safra, Previsão, Commodities agrícolas.

show abstract

“…Mevcut verinin türü ve boyutu göz önünde bulundurularak, eldeki sorunu çözmek için uygun algoritmayı seçmek çok önemlidir. Yüksek kaliteli ve niceliğe sahip veri kümeleri, çoğunlukla ML modellerinin doğruluğunu artırabilir (18) En yakın komşu algoritması: Çalışma mantığı, bir veri seti içerisinde bağımsız değişkenleri sınıflandırarak en yakın komşuların hangi sınıfta olduğunu tahmin etmeye dayanır (2). Araştırmada, bağımsız değişkenlerin CPV varlığı hakkındaki tahminlerinin doğruluk değerleri incelenmiştir.…”

Section: Makine öğRenmesiunclassified

CPV Tahmininde Makine Öğrenme Yöntemlerinin Performanslarının Değerlendirilmesi: Farklı Bölgeler ve Parametreler ile Büyük Veri Uygulaması

Zabzun¹,

Sevinç²,

Dalkılıç³

et al. 2022

JAIHS

View full text Add to dashboard Cite

Scopa: Nowadays, the interpretation, classification, storage and extraction of big data in different fields, which are rapidly increasing in regular and irregular areas, and making them useful again are among the subjects that are intensively studied. The correct interpretation of big data in the field of health is of vital importance as it enables fast and accurate diagnosis. In the project, machine learning methods that can interpret health data have been applied specifically to Canine parvovirus infection. While CPV can be diagnosed based on clinical findings, it needs to be supported by laboratory findings to distinguish it from other infections. Correct diagnosis is vital to distinguish CPV from other infections with bloody diarrhoea, which can result in death in puppies. For this reason, by analysing the virus together with other data that may be affected by the virus, the methods of making the most accurate decision were compared and evaluated.Purpose: In this study, it was aimed to interpret CPV, which is considered to be one of the most important infectious agents of dogs, popularly known as mad-head disease, using K-NearestNeighbour (KNN), RandomForest (RF), Logistic Regression and NaiveBayes classification algorithms in terms of different parameters. When the total accuracy values were examined, the accuracy rates decreased in logistic regression and RF methods when the insignificant variable was removed in the model.Result: RF method made the best predictions when Platelets, Platelet (PLT) variable was in the model.In cases where we do not want to remove this variable from the model, it can give us very efficient results. KNN method gives better results when the number of variables decreases. Especially when the data size increases, it has been observed that the machine learning method gives more efficient results with better performance.

show abstract

Machine learning-based farm risk management: A systematic mapping review

Cited by 19 publications

References 153 publications

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Análise e previsão do resultado financeiro da produção de soja

CPV Tahmininde Makine Öğrenme Yöntemlerinin Performanslarının Değerlendirilmesi: Farklı Bölgeler ve Parametreler ile Büyük Veri Uygulaması

Contact Info

Product

Resources

About