MDE-based Development of a Multispectral Camera for Precision Agriculture

Doering, D.; Vizzotto, Marcos Rodrigues; Bredemeier, Christian; Costa, Corrado; Henriques, Renato Ventura Bayan; Pignaton, Edison; Pereira, Carlos Eduardo

doi:10.1016/j.ifacol.2016.11.117

“…Examples of well-known DSLs are languages such as HTML and SQL, which are broadly applied. DSLs have also been used in various other more specific application domains such as landscape modeling (Gaucherel et al 2006), biology (Ananthanarayanan and Thies 2010; Bilitchenko et al 2011), environmental sciences (Channe et al 2015), and agricultural sciences (Doering et al 2013(Doering et al , 2016, and robotics (Nordmann et al 2014). Often a DSL framework is proposed, which is defined as a coherent set of DSLs focusing on the same problem domain but addressing specific concerns in that domain (Tekinerdogan and Arkin 2019).…”

Section: Introductionmentioning

confidence: 99%

A domain-specific language framework for farm management information systems in precision agriculture

Groeneveld¹,

Tekinerdoğan

²

,

Garousi

³

et al. 2020

View full text Add to dashboard Cite

Farm management information system (FMIS) is an important element of precision agriculture to support the decision making process in the agricultural business. Developing FMIS is not trivial and requires the proper design and implementation models for supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders. To cope with these challenges, a Domain-specific language (DSL) framework for the design and development of precision-agriculture FMISs is proposed and evaluated. The DSL framework is developed based on a domain-driven design approach in which a feature diagram is provided that represents the common and variant features of the precision agriculture domain. The key requirements for the DSL framework are discussed, the scope of the DSL is defined, and the set of DSLs for supporting FMISs is presented. A controlled experiment demonstrates that the proposed DSL framework is easy to learn and to use, and is effective in developing high-quality system models for precision agriculture.

show abstract

“…While there are literature reporting successful application of multi/hyper spectral image analysis for crop and weed detection [17][18][19], high quality non-visible spectrum imaging systems are generally more expensive and not-affordable to access -rather than visible ones -for researchers and farmers [20,21].…”

Section: Introductionmentioning

confidence: 99%

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Bakhshipour

¹

,

Zareiforoush

²

2020

Preprint

View full text Add to dashboard Cite

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

show abstract

“…While there are literature reporting successful application of multi/hyper spectral image analysis for crop and weed detection [17][18][19], high quality non-visible spectrum imaging systems are generally more expensive and not-affordable to access-rather than visible ones-for researchers and farmers [20,21]. Therefore, Optimizing the RGB image processing techniques for weed/crop detecting, is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Bakhshipour

¹

,

Zareiforoush

²

2020

View full text Add to dashboard Cite

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

show abstract

MDE-based Development of a Multispectral Camera for Precision Agriculture

Cited by 17 publications

References 14 publications

A domain-specific language framework for farm management information systems in precision agriculture

A domain-specific language framework for farm management information systems in precision agriculture

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Contact Info

Product

Resources

About