A dynamic artificial neural network for tomato yield prediction

Salazar, Raquel; Dannehl, Dennis; Schmidt, Uwe; López, I.; Rojano, A.

doi:10.17660/actahortic.2017.1154.11

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…It can effectively solve the problem of BP neural network easily falling into local minimal and slow convergence speed. In the WNN model, the mother wavelet function is shown in Equation (7).…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

“…The soft sensing of ANN techniques was widely applied to develop models for predicting different crop indicators, such as yield, growth, and other biophysical processes [5,6]. Salazar et al used a Levenberg-Marquardt algorithm with ANN to train and verify weights and perform bias adjustment and the ideal fresh fruit production result was obtained [7]. An ANN model was established to predict eight regression factors for pepper fruit yields by employing a large number of genotypes, and the results indicate that ANN with an 8:10:1 architecture achieved high accuracy [8].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

et al. 2021

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Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.

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Section: Wavelet Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

et al. 2021

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“…Chen proposed a tomato yield prediction model based on a wavelet and back-propagation (BP) neural network [28]. Salazar proposed a dynamic artificial neural network model based on a layered digital neural network (LDDN) to predict greenhouse tomato yields; the experimental results showed that LDDN can be used to predict greenhouse tomato yields one week in advance [29]. Liu captured video surveillance images of the tomato-ripening process: neural networks identified images and extracted growth features to identify the number of tomatoes hanging on plants, thereby establishing an estimation model of tomato yield [30].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

et al. 2023

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Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

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Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis

Belouz

Nourani

Zereg

et al. 2022

Scientia Horticulturae

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A dynamic artificial neural network for tomato yield prediction

Cited by 3 publications

References 12 publications

Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis

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