An Integrated Yield Prediction Model for Greenhouse Tomato

Lin, Dingyi; Wei, Ran; Xu, Li‐Hong

doi:10.3390/agronomy9120873

Cited by 31 publications

(30 citation statements)

References 35 publications

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“…thermometry and spectral vegetation indices for crop yield prediction because of their non-destructive and not labouror time-intensive characteristics. The feature group "Leave and Fruit Information" consists of the following variables: healthy and unhealthy image, leaf development, leaf area index, leaves and fruit mortality, number of leaves, number of fruits, number of stems [40]. Irrigation information refers to the number of open wells, irrigation ratio, net irrigated area, number of tanks, irrigation details, canals length, tube wells number and gross irrigated area [41].…”

Section: A Popular Features Used In Crop Yield Predictionmentioning

confidence: 99%

“…Extremely randomized trees (extra tree) (ERT) is an ensemble model as same as RF, but it utilizes unpruned decision trees. It splits Healthy and unhealthy image [44], leaf development [45], leaf area index [40], leaves and fruit mortality [40], number of leaves [40], number of fruits [40], number of stems [40].…”

Section: ) Machine Learning Algorithmsmentioning

confidence: 99%

“…Irrigation ratio [46], number of open wells (OW) [26] [41], number of tanks (TK) [ [40], GDD [24], growing degree days [46], killing degree days [46], day length [51] [13] [43], snow water [13], drought index (DI) [ [66], GCVI [50], VOD [46] [62], RVI [63] [39], GNDVI [63] [39], GRVI [63] [39], EVI2 [63], OSAVI [63] [39], WDRVI [63] [39], NDVIre [64], TSAVI [39], IPVI [39], MSAVI [39], GI [39], PVI [39], SAVI [39]], GESAVI [39], GLAI [39], CWSI [39], NDWI [39], GVI [39] LAI [42] [40] [65], FPAR [42], GPP [42], NIRv [56] [47], CDL [42], cropland census [42], satellite images from the landsat thematic mapper (TM) [42], satellite images from advanced wide field sensor (AWIFS) [42], empty-land [45], harrowed land [45], texture conditions [48], PVI [48].…”

Section: Irrigation Informationmentioning

confidence: 99%

“…Here, AlexNet was employed as the pre-trained model and the validation accuracy of the proposed system was 87.67%. Dingyi [40] proposed an integrated model consists of TOMGRO and Vanthoor model for greenhouse tomato yield prediction. However, the model does not result in sufficient modification of the greenhouse environment parameters.…”

Section: G Miscellaneous Crops Yield Predictionmentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

et al. 2021

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Section: A Popular Features Used In Crop Yield Predictionmentioning

confidence: 99%

Section: ) Machine Learning Algorithmsmentioning

confidence: 99%

Section: Irrigation Informationmentioning

confidence: 99%

Section: G Miscellaneous Crops Yield Predictionmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

et al. 2021

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“…On the other hand, various models have been devel-oped to predict and analyze many factors that affect growth and yield, and simulation models are also useful for quantitative analysis. Several models have been developed to predict yield and dry matter production in tomato (Dayan et al, 1993;Vanthoor et al, 2011;Lin et al, 2019), and efforts have also been made to analyze the potential yields in these models and the measured yield to analyze the reduction (Van Ittersum et al, 2013).…”

Section: Future Researchmentioning

confidence: 99%

A Review of Japanese Greenhouse Cucumber Research from the Perspective of Yield Components

Maeda

Ahn

2021

Hortic J

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Japanese cucumbers are unique in terms of their production methods, such as steamed cultivation in which the greenhouse is closed to increase the temperature and humidity. Moreover, Japan has strict standards for fruit size. Therefore, most research on greenhouse cucumbers in Japan has focused on pests, diseases and fruit quality, and few studies have focused on increasing yields. Therefore, we aimed to contribute to a yield improvement in Japanese greenhouse cucumber production by considering environmental factors and training methods based on the yield components. Here, we discuss different training systems, pinching and lowering methods, and the effects of environmental factors such as temperature, humidity, CO 2 concentration, irrigation, and nutrition on yield and yield components. Moreover, this paper also proposes future areas of research for Japanese greenhouse cucumbers.

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Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Mohmed

Grundy

Lotfi

et al. 2021

Advances in Intelligent Systems and Computing

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In any hydroponic system, effective plant growth and yield prediction are important in precision farming. Improving model growth systems can improve plant growth conditions for enhanced crop production and better quality food and increase resource use efficiency (e.g. light use efficiency, fertiliser use efficiency, water use efficiency) that meets the market demand with lower costs. Recently, different Artificial Intelligence (AI) techniques include machine learning and, deep learning methods are employed for providing powerful analytical tools. The proposed research in this study uses different machine/deep learning techniques to predict yield and plant growth variation for tomato crop (Tiny Tim mini tomato) under three different experimental conditions. The three tomato plants were grown under 3 different light treatments. The number of yielded fruits is predicted depends on the environmental conditions in each light treatment. This research deploys deep learning techniques includes Bidirectional Long short-term Memory (Bi-LSTM) with an attention mechanism for punctuation restoration and a standard Long Short-Term Memory (LSTM) recurrent neuron network. As well as, some other machine learning techniques including Support Vector Machine (SVM) and Random Forest (RF) for predicting the tomato yield. In the three different treatments, the tomato fruits yield growth and number yielded fruits values are used by the employed AI techniques to model the targeted growth parameters. The comparative results obtained from each technique are presented and discussed utilising the cross-validation process, to evaluate the performance achieved by the different methods. Very promising results, based on the data generated from the tomato growth and development with three light treatments are presented. The results that have been achieved are (97.8%) and (88.2%) achieved by employing the Bi-LSTM and LSTM algorithms, respectively.

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An Integrated Yield Prediction Model for Greenhouse Tomato

Cited by 31 publications

References 35 publications

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

A Review of Japanese Greenhouse Cucumber Research from the Perspective of Yield Components

Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

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