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

Abstract

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.

Study of genetic variability, heritability, and genetic advance for yield-related traits in tomato (Solanum lycopersicon MILL.)

“…The lack of genetic variability and unavailability of high-yielding cultivars are the main reasons for low seed production in Pakistan; hence, it is imperative to increase genetic variability to develop high-yielding tomato cultivars by evaluating available germplasm ( Brake et al, 2021 ; Kulus, 2022 ). Tomato yield is a multigenic trait and is greatly affected by environmental factors ( Wang et al, 2021 ). The breeders used potential hybridization techniques to obtain tomatoes with high-yield potential.…”

Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements

“…Fresh fruit yield = Dry fruit yield × DMC (9) The actual yields of the cultivars were recorded based on fruit harvested from 15 samples (five plants and three replicates) from 28 to 171 DAT. To measure the actual TDMs and LAIs, we sampled six plants from each cultivar at 51, 132, and 163 DAT and measured the leaf areas as well as fresh and dry weights of the tops (leaves, stems, and fruits).…”

“…The explanatory model consists of a quantitative description of the mechanisms and processes [4]. Several models have been developed to predict yield and dry matter (DM) production [4][5][6][7][8][9][10], especially, functional-structural plant models have been used these days widely [11], and these models have been improved in various methods [12][13][14][15]. On the other hand, a mechanical photosynthesis-based yield prediction model for cucumbers that simulated yield and fruit size as well as improved plant management, has been reported [16].…”

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

“…Alhnaity et al employed long short-term memory neural networks to model the target growth parameters of crops, and machine learning and deep learning were utilized to anticipate the yield and plant-growth variations across diverse scenarios [16]. To prevent the gradient descent learning algorithm from becoming trapped in local optima, Wang adopted a wavelet neural network with swifter convergence speed, and the wavelet neural network model enhanced by a genetic algorithm was proposed to elevate the prediction accuracy of tomato yields within a Chinese solar greenhouse [17]. Belouz et al integrated a neural network with a sensitivity analysis to forecast greenhouse tomato yields, and compared the results of the neural network model with that of a multiple linear regression model [18].…”