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Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Bini Darwin, Pamela Dharmaraj, Shajin Prince, Daniela Elena Popescu, Duraisamy Jude Hemanth
2021 Agronomy  
Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation.  ...  Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation.  ...  Deep Learning Models Deep learning models have been used in diverse applications of crop yield measurements such as crop monitoring, prediction, estimation and fruit detection in harvesting with numerous  ... 
doi:10.3390/agronomy11040646 fatcat:n3ru7ggspvgixlcu24meshbax4

Yield and Maturity Estimation of Apples in Orchards Using a 3-step Deep Learning-based Method

Xinxing Zhang, Zhuping Song, Qianyue Liang, Shumin Gao
2022 Quality Assurance and Safety of crops & foods  
In this paper, a 3-step deep learning–based approach for yield estimation and maturity classification is presented to address these issues.  ...  The presented workflow can be readily extended to other fruit crops for automation yield and maturity estimation featuring high efficiency and accuracy.  ...  In this work, we present a deep learning-based method to perform automatic in-field fruit yield and maturity estimation.  ... 
doi:10.15586/qas.v14i2.1008 fatcat:wzjx3yqhjzff3nwworwlw2s354

Central object segmentation by deep learning for fruits and other roundish objects [article]

Motohisa Fukuda, Takashi Okuno, Shinya Yuki
2020 arXiv   pre-print
The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of the original U-Net.  ...  , although CROP was trained solely by 172 images of fruits.  ...  Kazunari Adachi of the engineering department for giving us valuable legal advice concerinig this research project, and Hideki Murayama of the agricultural department for providing us with photos of fruits  ... 
arXiv:2008.01251v2 fatcat:s25dmmbvkrferoekauyj2xh7f4

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Caiwang Zheng, Amr Abd-Elrahman, Vance Whitaker
2021 Remote Sensing  
The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction  ...  Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management.  ...  [47] reviewed the use of deep learning in fruit detection and yield prediction.  ... 
doi:10.3390/rs13030531 fatcat:yts5pbuq2zhwrm6rt6c6hmkyti

A Survey of Deep Learning in Agriculture: Techniques and Their Applications

Chengjuan Ren, Dae-Kyoo Kim, Dongwon Jeong
2020 Journal of Information Processing Systems  
The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.  ...  This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics.  ...  al. [20] 2018 Machine learning approaches for crop yield A review of crop yield prediction prediction and nitrogen status estimation and nitrogen status estimation in precision agriculture: a review using  ... 
doi:10.3745/jips.04.0187 dblp:journals/jips/RenKJ20 fatcat:6qrdqx5m3rgivhs3qoi4qjzcnu

Applications of deep-learning approaches in horticultural research: a review

Biyun Yang, Yong Xu
2021 Horticulture Research  
recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks.  ...  In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety  ...  learning in yield estimation of horticultural crops No.  ... 
doi:10.1038/s41438-021-00560-9 pmid:34059657 fatcat:pxq52zxph5csnlps5olrcughkm

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images

Motohisa Fukuda, Takashi Okuno, Shinya Yuki
2021 Sensors  
Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times.  ...  The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net.  ...  About Deep Learning Deep learning (DL) is one of machine learning techniques. There are more and more applications of DL not only in fruit production but also in agriculture [32] .  ... 
doi:10.3390/s21216999 pmid:34770306 pmcid:PMC8586972 fatcat:h66punejozgkbk63n24n2rtjby

A New Perspective on Detection of Green Citrus Fruit in the Grove Using Deep Learning Neural Networks

Moshia Matshwene E., Mzini Loyiso L.
2021 Journal of Horticultural Science and Research  
Citrus quality, yield and disease management Estimating green citrus fruit yield at an earlier stage of fruit development can benefit growers to adjust site-specific management practices; and while it  ...  In addition, estimating the quantity of fruits in the citrus grove and potential fruit size before harvesting represents the basis for prediction of future fruit yield, planning of incomes, and calculation  ...  Recognizing green citrus fruits on a citrus tree in the grove is an important procedure in estimating the infections and fruit defects, a number of fruits for yield prediction, and make economic estimates  ... 
doi:10.36959/745/412 fatcat:ww7e4pkjqbd6rnf2a6o7csqana

An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest [article]

Ho Seok Ahn, Feras Dayoub, Marija Popovic, Bruce MacDonald, Roland Siegwart, Inkyu Sa
2018 arXiv   pre-print
Specifically, our work focuses on sensing and perception in the three main horticultural procedures: pollination, yield estimation, and harvesting.  ...  Horticultural enterprises are becoming more sophisticated as the range of the crops they target expands.  ...  In the next section, we examine the new shift towards deep learning for yield estimation applications. B.  ... 
arXiv:1807.03124v1 fatcat:m4eojazb2fdtnc7zahvxlgjl5y

Application of Artificial Intelligence in Fruit Production: A Review

S. Manonmani, S. Senthilkumar, U.S. Akshara Govind, S. Manivannan
2022 Agricultural Science Digest - A Research Journal  
In recent years AI powered systems aids the farmers in all process of fruit production such as in irrigation scheduling through smart irrigation systems, detection and diagnosis of pest and diseases, weed  ...  A data by UN FAO proclaims that the availability of land area for the crop cultivation will be 4% in 2050 with additional population of 2 billion to the existing global population.  ...  Most of the fruit growers estimate their yield by counting fruits during the early fruit drop.  ... 
doi:10.18805/ag.d-5482 fatcat:euviios25rftnev4dqsidytxtq

Identification of Citrus Canker on Citrus Leaves and Fruit Surfaces in the Grove Using Deep Learning Neural Networks

Matshwene Edwin Moshia, Loyiso Lloyd Mzini
2020 Journal of Agricultural Science and Technology: B  
The authors are optimistic that identification of citrus canker on citrus leaves and fruit surfaces in the grove could be one of the agricultural problems that can potentially benefit from deep learning  ...  A standard strategy in deep learning neural networks is to run the learning algorithm with many optimization parameters and pick the model that gives the best performance on a validation set.  ...  Accurate citrus fruit yield estimate before harvest is valuable to growers and decision makers [22] .  ... 
doi:10.17265/2161-6264/2020.01.006 fatcat:ydoso6rhjjblffaxkauqxh4c7m

Artificial Intelligence to Improve the Food and Agriculture Sector

Rayda Ben Ayed, Mohsen Hanana
2021 Journal of Food Quality  
Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations  ...  and deep Q-learning.  ...  Recently, different ML algorithms are used for crop yield prediction such as the Bayesian network, regression, decision tree, clustering, deep learning, and ANN [16] [17] [18] .  ... 
doi:10.1155/2021/5584754 doaj:0a966d4e11d14561b43c4c583f533232 fatcat:qua2nk3pn5bpvgsk4boki7kgey

Arecanut Bunch Segmentation Using Deep Learning Techniques

Anitha A. C., R. , Dhanesha, Shrinivasa Naika C. L., Krishna A. N., Parinith S. Kumar, Parikshith P. Sharma
2022 North atlantic university union: International Journal of Circuits, Systems and Signal Processing  
This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing  ...  Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities  ...  transform to identify and estimate the yield of the fruits has been proposed [25] .  ... 
doi:10.46300/9106.2022.16.129 fatcat:i347lhkx4bgvlkprefdluf2x7e

Smartphone for palm oil fruit counting to reduce embezzlement in harvesting season

Aripriharta Aripriharta, Adim Firmansah, Nandang Mufti, Gwo-Jiun Horng, Norzanah Rosmin
2020 Bulletin of Social Informatics Theory and Application  
Meanwhile, companies are required to calculate crop yields quickly and accurately.  ...  Harvest estimation is an essential parameter in the agriculture industries to estimate transportation facilities and storage areas in the harvesting season.  ...  Unlike the above three investigations, [15] applied the R-CNN Faster model and Single Shot Detector (SSD) to calculate crop yield estimates.  ... 
doi:10.31763/businta.v4i2.283 fatcat:5xdfkh7zqjfx5jgymowlmji4r4

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Normaisharah Mamat, Mohd Fauzi Othman, Rawad Abdoulghafor, Samir Brahim Belhaouari, Normahira Mamat, Shamsul Faisal Mohd Hussein
2022 Agriculture  
Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly  ...  Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing.  ...  [77] reviewed the application of deep learning in fruit detection and yield estimation, Zhang et al.  ... 
doi:10.3390/agriculture12071033 fatcat:kdbt3pqdz5hurmxt3jss3ez7ne
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