Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

Pourdarbani, Razieh; Sabzi, Sajad; Hernández-Hernández, Mario; Hernández-Hernández, José Luis; Garcı́a-Mateos, Ginés; Kalantari, Davood; Molina-Martínez, José Miguel

doi:10.3390/rs11212546

Cited by 26 publications

(13 citation statements)

References 37 publications

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“…A better prediction algorithm for corn, soybean [99] and paddy crops was proposed with a (feed forward back propagation) artificial neural network (ANN) and later with a fusion of multiple linear regression (MLR). The linear discriminant analysis (LDA) approach eradicates the imbalance generated from the performance value attained through an ANN classifier [100]. The fusion of huge datasets was implemented and compared with various machine learning models like SVM, DL, extremely randomized trees (ERT) and random forest (RF) for the estimation of corn yield [36].…”

Section: Deep Architectures In Smart Farmingmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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Section: Deep Architectures In Smart Farmingmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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“…After segmentation, different color and texture properties (features) of the chickpea samples were extracted. The extracted color properties follow next: mean and standard deviation of the first, second, third channels (components), and the mean of all three channels for RGB, HSV, HSI, YCbCr, CMY, and YIQ color spaces [16]. Table 1 defines the six color spaces used in the present study, including proper transformation equations from RGB color space.…”

Section: Extraction Of Different Properties Of Each Chickpea Sample Imentioning

confidence: 99%

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Pourdarbani

Kalantari

Hernández-Hernández

et al. 2020

Foods

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Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

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“…The DSS uses a multiple classifier system that estimates the final crop type map based on majority voting, using results from both RF and SVM. This approach usually allows crop discrimination with higher credibility than using single classifier approaches [46,47]. Nevertheless, the user can interfere and select to use either RF or SVM for the crop type discrimination based on the specific data characteristics.…”

Section: Crop Mapping Functionality Overviewmentioning

confidence: 99%

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

et al. 2020

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The successful implementation of the European Commission's Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters' effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user's needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers' subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user's requirements, and wishes to stimulate and inspire further research and development.Sustainability 2020, 12, 1233 2 of 20 filed inspections may determine the eligible areas for subsidy or compensation precisely, however they require a lot of effort and time and some factors like the adverse weather conditions, or remote areas, may cause large delays in the payments, which has an impact on the agricultural processes and development of the country. [1,2]. Lately, the control efforts involve the examination of satellite imagery, but this refers to very high-resolution commercial imagery and visual image interpretation or rare case image classification of single-date images [3]. These procedures improve the control efforts in some cases, but they have a high cost and their effectiveness relies mostly on the skills of the photo-interpreter and therefore cannot always offer timely, reliable, and robust results [4].There are several attempts for automating the mapping procedures in agriculture-related applications. For example, in terms of crop types mapping, the EU is coordinating the Monitoring Agricultural Resources (MARS) initiative [5] with the help of the Joint Research Center (JRC) in order to facilitate the coordination of the CAP. In the framework of MARS and following guidelines from the JRC, each EU member state is building a geographic information Land Parcel Identification System (LPIS) by interpreting very-high-resolution satellite images. Along with LPIS, the JRC is in charge of the image acquisition and it provides scientific and technical suppor...

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Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

Cited by 26 publications

References 37 publications

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

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