A convolutional neural network approach to detecting fruit physiological disorders and maturity in ‘Abbé Fétel’ pears

Bonora, Alessandro; Bortolotti, G.; Bresilla, Kushtrim; Grappadelli, Luca Corelli; Manfrini, Luigi

doi:10.1016/j.biosystemseng.2021.10.009

Cited by 9 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…organoleptic, nutritional, visual or non-visual defects) [42] during fruit development, ripening and post-harvest conservation [36,37]. For example, a convolutional neural network has been trained to evaluate fruit maturity based on starch index in apple and pear [43].…”

Section: Introductionmentioning

confidence: 99%

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Bouillon,

Fanciullino,

Belin

et al. 2024

Plant Methods

View full text Add to dashboard Cite

Background The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. Results In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits’ flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Conclustion Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

show abstract

Section: Introductionmentioning

confidence: 99%

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Bouillon,

Fanciullino,

Belin

et al. 2024

Plant Methods

View full text Add to dashboard Cite

show abstract

“…The method based on manual features mainly relies on manual design and extraction of visual features of the target, which are usually more intuitive, such as color, texture, shape, etc. Commonly used manual features include local binary patterns (LBPs) [10], scale invariant feature transformations (SIFTs) [11], directional gradient histograms (HOGs) [12]., and so on. Based upon these manual features, various detectors can be designed to match and identify specific targets.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Algorithm for Real-Time Prediction of Fish Yield in All Cage Schools

Wang,

Chen,

Peng

et al. 2024

JMSE

View full text Add to dashboard Cite

Cage aquaculture makes it easier to produce high-quality aquatic products and allows full use of water resources. 3Therefore, cage aquaculture development is highly valued globally. However, the current digitalization level of cage aquaculture is low, and the farming risks are high. Research and development of digital management of the fish population in cages are greatly desired. Real-time monitoring of the activity status of the fish population and changes in the fish population size in cages is a pressing issue that needs to be addressed. This paper proposes an improved network called CC-YOLOv5 by embedding CoordConv modules to replace the original ConV convolution modules in the network, which improves the model’s generalization capability. By using two-stage detection logic, the target detection accuracy is enhanced to realize prediction of the number of fish populations. OpenCV is then used to measure fish tail lengths to establish growth curves of the fish and to predict the output of the fish population in the cages. Experimental results demonstrate that the mean average precision (mAP) of the improved algorithm increases by 14.9% compared to the original YOLOv5, reaching 95.4%. This research provides an effective solution to promote the intelligentization of cage aquaculture processes. It also lays the foundation for AI (Artificial Intelligence) applications in other aquaculture scenarios.

show abstract

“…At present, the most mainstream object detection methods have been widely applied in various fields, such as population counting, wheat ear counting, fruit counting, etc. The object detection method itself has two functions: detection and classification, which can detect where the target object is and identify which category it belongs to [ 12 ]. Common object detection methods are mainly divided into one-stage and two-stage object detection methods.…”

Section: Introductionmentioning

confidence: 99%

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Zheng

et al. 2023

Sensors

View full text Add to dashboard Cite

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8’s backbone to enhance the network’s ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.

show abstract

A convolutional neural network approach to detecting fruit physiological disorders and maturity in ‘Abbé Fétel’ pears

Cited by 9 publications

References 24 publications

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Improved YOLOv5 Algorithm for Real-Time Prediction of Fish Yield in All Cage Schools

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Contact Info

Product

Resources

About