An intelligent fruits classification in precision agriculture using bilinear pooling convolutional neural networks

Prakash, Achanta Jyothi; Periasamy, P.

doi:10.1007/s00371-022-02443-z

Cited by 21 publications

(9 citation statements)

References 47 publications

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“…The category "Other Fruits" corresponds to fruits that are analyzed in a single article, usually fruits of local origin. Agrivita 1 [115] Applied Intelligence 1 [116] Computational Intelligence and Neuroscience 1 [117] Computer Systems Science and Engineering 1 [118] Computers Electrical Engineering 1 [119] Data in Brief 1 [42] Ecological Informatics 1 [120] Electronics 1 [121] Energy Reports 1 [122] European Food Research and Technology 1 [123] Expert Systems With Applications 1 [124] Frontiers in Robotics and AI 1 [125] Horticulturae 1 [126] Journal of Robotics and Mechatronics 1 [135] Jurnal Kejuruteraan 1 [136] Mathematical Problems in Engineering 1 [137] Continued on next page Mechanical Systems and Signal Processing 1 [139] Multimedia Systems 1 [140] Neural Computing Applications 1 [141] Neural Network World 1 [142] Neural Networks 1 [143] Neurocomputing 1 [144] Plant and Cell Physiology 1 [145] Plants 1 [146] Postharvest Biology and Technology 1 [147] Procedia Computer Science 1 [148] Remote Sensing 1 [149] Results in Engineering 1 [150] Scientia Horticulturae 1 [151] Scientific African 1 [152] Scientific Programming 1 [153] Scientific Reports 1 [154] Sn Applied Sciences 1 [50] Sustainability 1 [155] Traitement du Signal 1 [156] Visual Computer 1 [52]…”

Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

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Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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“…Object detection often makes use of deep learning, which is a recent and modern technique for image processing and data analysis [237]. The main architectures applied in deep learning there are recurrent neural networks, also known by the term RNN (Recurrent Neural Network), short and long term neural networks, also known by the term LSTM (Long Short Term Memory) and convolutional neural networks, also called CNN (Convolutional Neural Network) [90,255]. There are several platforms that provide the ability to develop neural networks, among them is GoogLeNet, created by Google [256], which introduced important improvements to convolutional neural networks, however, an optimisation of the latter is available, which is Inception v3 [257].…”

Section: Deep Learning In Viticulturementioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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“…These models can be used in health monitoring applications to observe fruit intake and calorie estimation. The data can be used by machine learning researchers/companies to develop models for recognizing different fruits [4] , [5] . The current research trends in deep learning and machine learning target mainly the development of applications for everyday use such as face recognition, fingerprint recognition, or application in the fields of healthcare, engineering, and many others.…”

Section: Value Of the Datamentioning

confidence: 99%

DeepFruit: A dataset of fruit images for fruit classification and calories calculation

Latif,

Mohammad,

Alghazo

2023

Data in Brief

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An intelligent fruits classification in precision agriculture using bilinear pooling convolutional neural networks

Cited by 21 publications

References 47 publications

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

DeepFruit: A dataset of fruit images for fruit classification and calories calculation

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