Premonition Net, a multi-timeline transformer network architecture towards strawberry tabletop yield forecasting

Onoufriou, George; Hanheide, Marc; Leontidis, Georgios

doi:10.1016/j.compag.2023.107784

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Machine learning for other industrial applications has also been found to be useful for prediction. For example, predicting the compressive strength of concrete containing nano silica using support vector machines and Gaussian process regression [23], predicting the mechanical behaviour of 3D-printed components [24], predicting elemental stiffness matrix of functionally graded nanoplates [25], optimising industrial refrigeration systems [26], forecasting strawberry yield [27], and non-intrusive nuclear reactor monitoring [28].…”

Section: Machine Learning In Industrymentioning

confidence: 99%

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Potts

Hackney

Leontidis

2023

MAKE

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Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compared an existing predictive emissions model, a first-principles-based Chemical Kinetics model, against two machine learning models we developed based on the Self-Attention and Intersample Attention Transformer (SAINT) and eXtreme Gradient Boosting (XGBoost), with the aim to demonstrate the improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques and determine whether XGBoost or a deep learning model performs the best on a specific real-life gas turbine dataset. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.

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Section: Machine Learning In Industrymentioning

confidence: 99%

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Potts

Hackney

Leontidis

2023

MAKE

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“…The premonition net is employed to learn strawberry recognition results from past, present, and future moments. Combined with the transform structure, it achieves the prediction of strawberry growth conditions [20].…”

Section: Introductionmentioning

confidence: 99%

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Wang,

Han,

et al. 2024

Agriculture

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Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

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“…In this situation, farmers need timely advice to figure out how productive their crops will be in the future, and a study needs to be carried out to assist farmers for enhanced returns. Therefore, predicting crop yields is an important aspect in agriculture (Onoufriou et al. , 2023; Kouadio et al.…”

Section: Introductionmentioning

confidence: 99%

Analyzing predictors of pearl millet supply chain using an artificial neural network

Dhankar,

Routroy,

Sharma

2024

JM2

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Purpose The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield. Design/methodology/approach Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them. Findings The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R2 values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield. Research limitations/implications To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield. Originality/value The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

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Premonition Net, a multi-timeline transformer network architecture towards strawberry tabletop yield forecasting

Cited by 11 publications

References 11 publications

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Analyzing predictors of pearl millet supply chain using an artificial neural network

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