Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

The objective was to implement an artificial neural network to improve the recognition of geometric figures that later allow making estimates about shapes in a real context to create virtual planes. A Matlab program was developed to create the neural network and two knowledge bases containing fifteen different geometric figures that were developed for the training stage and for the recognition stage. The results of the training phase were carried out in three processes, obtaining a percentage of 11.35%, 3.55%, and 2% error margin respectively, later came the recognition stage with three processes, obtaining 40%, 100%, and 100% figures recognized respectively. It is concluded that the implemented neural network performed the recognition of fifteen geometric figures correctly (100%), requiring three training processes and three recognition processes to verify their learning. Keywords: Image recognition, Machine learning, artificial neural networks, artificial intelligence.

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“…Una función de activación que ayuda a limitar los valores de salida. Un umbral que es una constante encima de la cual la neurona se activa [17].…”

Section: Estructura De La Red Neuronalunclassified

Application of an artificial neural network in the recognition of geometric figures

Adrianzén-Olano

Cubas

Huerta

et al. 2022

uct

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“…An autonomous artificial sorting system can perform effective segmentation from images of blackberry in its different growth processes. Such a system, easy to use and very low cost, can support the production and commercialization of the fruit in its different stages [9], [10]. This type of system should consider a robust and low-cost portable design [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network based Detection System for the Visual Diagnosis of the Blackberry

Rubio¹,

Avendaño²,

Sarmiento³

2022

IJACSA

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Thanks to its geographical and climatic advantages, Colombia has a historically strong fruit-growing tradition. To date, the basis of its food and economic development in a significant part of its territory is based on a wide range of fruits. One of the most important in the central and western regions of the country is the blackberry, which is rooted not only from the economic and food point of view but also culturally. For the departments of Casanare, Santander, and Cundinamarca, this fruit is one of the primary sources of income, rural employment, and food supply and income. However, small and medium farmers cultivate without access to technological production tools and with limited economic capacity. This process suffers from several problems that affect the whole plant, especially the fruit, which is strongly influenced by fungi, extreme ripening processes, or low temperatures. One of the main problems to be dealt with in its cultivation is the spread of pests, which are one of the causes of fruit rot. As a support strategy in producing this fruit, the development of an embedded system for visually diagnosing the fruit using a deep neural network is proposed. The article presents the training, tuning, and performance evaluation of this convolutional network to detect three possible fruit states, ripe, immature, and rotten, to facilitate the harvesting and marketing processes and reduce the impact on the healthy fruit and the quality of the final product. The model is built with a ResNet type network, which is trained with its dataset, which seeks to use images captured in their natural environment with as little manipulation as possible to reduce image analysis. This model achieves an accuracy of 70%, which indicates its high performance and validates its use in a stand-alone embedded system.

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“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

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Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

Cited by 5 publications

References 16 publications

Application of an artificial neural network in the recognition of geometric figures

Application of an artificial neural network in the recognition of geometric figures

A Deep Neural Network based Detection System for the Visual Diagnosis of the Blackberry

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

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