Artificial neural network modelling in the prediction of bananas’ harvest

Souza, Angela Vacaro de; Neto, Alfredo Bonini; Piazentin, Jhonatan Cabrera; Dainese, Bruno José; Gomes, Éder Pereira; Bonini, Carolina dos Santos Batista; Putti, Fernando Ferrari

doi:10.1016/j.scienta.2019.108724

Cited by 26 publications

(21 citation statements)

References 13 publications

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“…In a complementary study using the modeling of artificial neural networks to evaluate banana gestation data, the authors verified that this model presented unprecedented results, with a low error in the average gestation time of the bunch, thus becoming a tool for producers to manage their production. This complementary study obtained a good performance in the training process (Wi weights all adjusted) with an error (MSE) of 0.00397 and an R value of 0.8998, resulting in a strongly positive linear correlation (Souza et al, 2019).…”

Section: Resultsmentioning

confidence: 83%

Mobile App for the Prediction of Bananas Harvest

Santos

Matulovic

Vieira

et al. 2020

Int J Innov Educ Res

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Section: Resultsmentioning

confidence: 83%

Mobile App for the Prediction of Bananas Harvest

Santos

Matulovic

Vieira

et al. 2020

Int J Innov Educ Res

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“…Bananas are a popularly marketed fresh fruit grown in more than 120 countries around the world [1]. The global production of bananas in 2017 is about 113 million tons [2] and is of significant economic and social value.…”

Section: Introductionmentioning

confidence: 99%

Effects of Morphological and Anatomical Characteristics of Banana Crown Vascular Bundles on Cutting Mechanical Properties Using Multiple Imaging Methods

et al. 2020

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To obtain the appropriate mechanized cutting region for banana dehanding, the methods of X-ray Computed Tomography (CT), Paraffin-embedded tissue section, and scanning electron microscopy (SEM) were adopted to observe the morphological and anatomical characteristics of vascular bundles of the banana crown. The results indicated that the crown can be divided into three regions, viz., the central stalk–crown transition region (CSCTR), the crown expansion region (CER), and the crown–finger transition region (CFTR). According to the obtained characteristics, the cutting mechanical properties are found to be affected by the relative angle between the vascular bundle and cutter (RAVBC) and the vascular bundle density. In CSCTR, due to the opposite change of RAVBC and density, the cutting mechanical properties become unstable and the cutting energy decreases gradually from 4.30 J to 2.57 J. While in CER, the cutting mechanical properties tend to be stable, and the cutting energy varies in a small range (2.83–2.92 J), owing to the small changes of RAVBC and density. When the vascular bundles cross from the CER to CFTR, the cutting energy increases with the increase of RAVBC and density, which varies from 3.37 to 4.84 J. Accordingly, the appropriate cutting region for dehanding, which can reduce the energy consumption and improve the cutting efficiency, is ascertained to be between CSCTR and CER.

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“…Muitos estudos têm sido reportados sobre o uso de inteligência artificial na agricultura (Bala et al, 2005;Diamantopoulou, 2005;Movagharnejad & Nikzad, 2007;Zhang et al, 2007), sendo muitos destes estudos dedicados a predições (Jiang et al, 2004;Uno et al, 2005;Savin et al, 2007;De Souza et al 2019). No Brasil, alguns trabalhos têm sido desenvolvidos na área da ciência do solo.…”

Section: Introductionunclassified

“…Foi proposto o desenvolvimento de uma RNA com o objetivo de analisar dados de solos em recuperação de forma que se possa classificá-los automaticamente em função de seus atributos físicos. Em De Souza et al 2019 foi proposto uma RNA para estimar os dias da colheita dos cachos da banana em função dos dados meteorológicos. O erro médio na fase de operação da rede foi em torno de 0,0596, ou seja, 6% de erro em comparação da saída obtida em relação à saída desejada.…”

Section: Introductionunclassified

Análise da recuperação física de solos degradados via Redes Neurais Artificiais por meio de uma interface gráfica

Chitero

Neto

Bonini

et al. 2020

RSD

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Proper soil management techniques are essential to keep the soil healthy and without degradation. When this is not possible, this soil must be recovered, taking into account the attributes of the soil and its regenerative power, with this, several techniques are being used. In this context, this work aims to develop an interactive program (analyze and classify) using Artificial Neural Networks (ANN) to estimate soil recovery levels (recovered (R), partially recovered (PR) and not recovered (NR) as a function of physical attributes. The experiment was carried out at the São Paulo Agribusiness Technology Agency - APTA do Extremo Oeste, in Andradina / SP from 2015 to 2017, in soil classified as Ultisol cultivated with Urochloa pasture, with different ways of introducing Estilosantes cv. Campo Grande. The soil attributes studied were: soil density, soil porosity, mechanical resistance to penetration, water infiltration in the soil and weighted average diameter in the soil layers: 0-10; 0.10-0.20 and 0.20-0.40 m The program was developed in the MATLAB environment and the simulation was performed using a graphical interface. and work was the multilayer Perceptron (MLP). It was found that the network achieved adequate training, with a low mean square error, which could generate an interesting and automatic alternative for the classification and analysis of recovering soils. The results were printed on a self-explanatory graphical interface, with graphs and metadata of the physical indexes and their classifications regarding ANN.

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Artificial neural network modelling in the prediction of bananas’ harvest

Cited by 26 publications

References 13 publications

Mobile App for the Prediction of Bananas Harvest

Mobile App for the Prediction of Bananas Harvest

Effects of Morphological and Anatomical Characteristics of Banana Crown Vascular Bundles on Cutting Mechanical Properties Using Multiple Imaging Methods

Análise da recuperação física de solos degradados via Redes Neurais Artificiais por meio de uma interface gráfica

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