Yield Prediction for Tomato Greenhouse Using EFuNN

Qaddoum, Kefaya; Hines, Evor L.; Iliescu, Daciana

doi:10.1155/2013/430986

Cited by 30 publications

(13 citation statements)

References 19 publications

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“…Most researches have tried to find the features highly correlated with yield using UAV data and reported a positive correlation between vegetation indices and both biomass and yield [30][31][32]. A few studies also focus on extracting discrete phenotypic data, such as canopy cover, canopy height, and vegetation indices, related to biomass and yield for tomato [21,33]. Although they could predict biomass and yield using UAV-derived phenotypes and environment conditions, data volume was limited to consider the whole growing cycle.…”

Section: Correlation Coefficient Of Phenotypic Features Andmentioning

confidence: 99%

Unmanned Aircraft System‐ (UAS‐) Based High‐Throughput Phenotyping (HTP) for Tomato Yield Estimation

et al. 2021

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Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

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Section: Correlation Coefficient Of Phenotypic Features Andmentioning

confidence: 99%

Unmanned Aircraft System‐ (UAS‐) Based High‐Throughput Phenotyping (HTP) for Tomato Yield Estimation

et al. 2021

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“…While in [ 17 ], ANN has been applied to predict the pepper fruit yield based on factors such as fruit water content, days to flowering initiation, and so on. An Evolving Fuzzy Neural Network (EFuNN) was proposed in [ 18 ] for automatic tomato yield prediction, given different environmental variables inside the greenhouse, namely, temperature, CO

, vapour pressure deficit (VPD), and radiation, as well as past yield. A Dynamic Artificial Neural Network (DANN) [ 19 ] was implemented to predict tomato yields, based on a series of predictors such as CO

fixation, transpiration, solar radiation as well as past yield.…”

Section: Literature Workmentioning

confidence: 99%

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

Gong

Jiang

et al. 2021

Sensors

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Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.

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“…However, in contrast to the hypothesis proposed in the present study, most studies relate yield prediction not only with plant yield factors but also with environmental factors (Higashide. 2009;Qaddoum et al, 2013;Hussain & Hatibaruah, 2015).…”

Section: Introductionmentioning

confidence: 99%

Yield prediction of experimental plots based on the harvest of specific fruit clusters for selection of fresh market tomato hybrids

et al. 2021

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The objective of this study was to propose models based on the harvest of specific fruit clusters to estimate the plot yield of trials containing fresh market tomato hybrids. Three experiments were conducted at the Experimental Station of Syngenta Crop Protection in the municipality of Holambra, state of São Paulo, Brazil, in 2016 and 2017. The experimental design was randomized complete blocks with 12 genotypes (experiments 1 and 3) and 13 genotypes (experiment 2), with 4 replicates and 10 plants per plot. Multiple linear regression models were fitted (stepwise method) with experiment 1 data (cross-validation), the best models were selected (higher adjusted R²) and then tested with experiments 2 and 3 data by mean of absolute percentage error (MAPE), for the traits mean weight of marketable fruit per plant A (MWA), AA (MWAA), AAA (MWAAA), AA and AAA (MW23A), and the ratio between the market weight and the number of marketable fruits (MFW). The models with four fruit clusters (2, 3, 6 and 9) showed the best balance between prediction capacity and the number of fruit clusters to harvest. The traits MWAA, MW23A and MFW generated reliable predictions, with MAPE approximately 5%. The multiple linear regression can be used to estimate the plot yield what, in the last instance, contributes to the reduction of the costs to conduct fresh market tomato hybrids

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Yield Prediction for Tomato Greenhouse Using EFuNN

Cited by 30 publications

References 19 publications

Unmanned Aircraft System‐ (UAS‐) Based High‐Throughput Phenotyping (HTP) for Tomato Yield Estimation

Unmanned Aircraft System‐ (UAS‐) Based High‐Throughput Phenotyping (HTP) for Tomato Yield Estimation

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

Yield prediction of experimental plots based on the harvest of specific fruit clusters for selection of fresh market tomato hybrids

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