Advancing precision agriculture: The potential of deep learning for cereal plant head detection

Sanaeifar, Alireza; Guindo, Mahamed Lamine; Bakhshipour, Adel; Fazayeli, Hassan; Liu, Yuanyuan; Yang, Ce

doi:10.1016/j.compag.2023.107875

Cited by 31 publications

(10 citation statements)

References 130 publications

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“…If combined with the preprocessing process, the dataset variations will increase. For this reason, the addition of these two features has its own advantages and disadvantages relative to the characteristics of the dataset being developed (Sanaeifar et al, 2023). Need to choose the right combination of preprocessing and augmentation processes in order to improve model performance and not add too much time to the training process.…”

Section: Adjustment Of Preprocessing and Augmentation Processmentioning

confidence: 99%

Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots

Purwansya,

Solahudin,

Supriyanto

2024

JTEP-L

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As the demand for shallot increases, wide-scale cultivation area must be managed efficiently. However, shallot productivity decreases every year because of plant diseases. Fusarium disease has an intensity up to 60% and can affect yield losses up to 50%. This study was conducted to develop the fusarium disease detection system for shallot using deep learning model and analyze the effect of preprocessing and augmentation adjustment. This study used YOLOv5 deep learning algorithm consisting of the following stages: (1) dataset acquisition, (2) dataset annotation, (3) dataset preprocessing and augmentation, (4) dataset training and validation, and (5) model testing and evaluation. A total 9,664 annotated dataset was trained to YOLOv5m pre-trained weights. Based on testing and evaluation results, precision, recall, and mean average precision (mAP) metrics of the model without preprocessing and augmentation were 55.5%; 54%; and 48.3% respectively. Metric values of the model were increased to 57.6%; 58.4%; and 54.1% respectively with adjustment of preprocessing and augmentation combination process. Percentage increase in metrics when compared to the control model for each value of precision, recall, and mAP were 2.1%; 4.4%; and 5.8%. This shows a significant impact on the addition of preprocessing and augmentation processes that match the characteristics of the dataset to increase the value of model performance. Keywords: Augmentation, Deep learning, Fusarium, Shallot.

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Section: Adjustment Of Preprocessing and Augmentation Processmentioning

confidence: 99%

Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots

Purwansya,

Solahudin,

Supriyanto

2024

JTEP-L

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“…Artificial intelligence and computer techniques enable the use of precision agriculture technologies in fertilisation, the application of plant protection products, and precise agricultural and technical operations such as sowing, planting potatoes or vegetable seedlings, etc. [ 44 , 45 , 46 , 47 , 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

Rybacki,

Niemann,

Derouiche

et al. 2024

Sensors

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The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.

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“…Computer techniques and artificial intelligence enable the use of precision agriculture in sustainable fertilization and point application of plant protection products, as well as precise agrotechnical treatments, e.g. sowing, planting seedlings (Osuch et al 2020, Rybacki et al 2021, Coulibaly et al 2022, Rybacki et al 2022, Gkillas et al 2023, Raptis et al 2023, Sanaeifar et al 2023.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

Rybacki,

Sawinska,

Kačániová

et al. 2024

AFSci

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The main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consistingof an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.

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Advancing precision agriculture: The potential of deep learning for cereal plant head detection

Cited by 31 publications

References 130 publications

Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots

Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

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