982 research outputs found
Fruit sizing using AI: A review of methods and challenges
Fruit size at harvest is an economically important variable for high-quality table fruit production in orchards and vineyards. In addition, knowing the number and size of the fruit on the tree is essential in the framework of precise production, harvest, and postharvest management. A prerequisite for analysis of fruit in a real-world environment is the detection and segmentation from background signal. In the last five years, deep learning convolutional neural network have become the standard method for automatic fruit detection, achieving F1-scores higher than 90 %, as well as real-time processing speeds. At the same time, different methods have been developed for, mainly, fruit size and, more rarely, fruit maturity estimation from 2D images and 3D point clouds. These sizing methods are focused on a few species like grape, apple, citrus, and mango, resulting in mean absolute error values of less than 4 mm in apple fruit. This review provides an overview of the most recent methodologies developed for in-field fruit detection/counting and sizing as well as few upcoming examples of maturity estimation. Challenges, such as sensor fusion, highly varying lighting conditions, occlusions in the canopy, shortage of public fruit datasets, and opportunities for research transfer, are discussed.This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017 SGR 646 and 2021 LLAV 00088) and by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / FEDER (grants RTI2018-094222-B-I00 [PAgFRUIT project] and PID2021-126648OB-I00 [PAgPROTECT project]). The Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and European Social Fund (ESF) are also thanked for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio
EVALUATING THE PERFORMANCE OF A SEMI-AUTOMATIC APPLE FRUIT DETECTION IN A HIGH-DENSITY ORCHARD SYSTEM USING LOW-COST DIGITAL RGB IMAGING SENSOR
This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina’s Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil’s apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I1 , I2 , I3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I”2 , I”3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I”2 , I”3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed a 0.73 coefficient of determination (R2 ), and fruit recognition accuracy rate showed 0.96 R2 . The proposed approach provides results with great applicability for small holder farms and may support local harvest prediction
Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular Economy
Fruit detection is crucial for yield estimation and fruit picking system performance. Many
state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper
presents the results for peach detection by applying a faster R-CNN framework in images captured
from an outdoor orchard. Although this method has been used in other studies to detect fruits, there
is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and
distributions in trees are particular, the development of a fruit detection procedure is specific. The
results show great potential in using this method to detect this type of fruit. A detection accuracy of
0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture
applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate
climate change, due to horticultural activities by accurate product prediction, leading to improved
resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce
food loss and waste via improved agricultural activity scheduling.The authors are thankful to Fundação para a Ciência e Tecnologia (FCT) and
R&D Unit “Center for Mechanical and Aerospace Science and Technologies” (C-MAST), under
project UIDB/00151/2020, for the opportunity and the financial support to carry on this project. The
contributions of Hugo Proença and Pedro Inácio in this work were supported by FCT/MEC through
FEDER—PT2020 Partnership Agreement under Project UIDB//50008/2021.info:eu-repo/semantics/publishedVersio
Machine Vision-Based Crop-Load Estimation Using YOLOv8
Labor shortages in fruit crop production have prompted the development of
mechanized and automated machines as alternatives to labor-intensive orchard
operations such as harvesting, pruning, and thinning. Agricultural robots
capable of identifying tree canopy parts and estimating geometric and
topological parameters, such as branch diameter, length, and angles, can
optimize crop yields through automated pruning and thinning platforms. In this
study, we proposed a machine vision system to estimate canopy parameters in
apple orchards and determine an optimal number of fruit for individual
branches, providing a foundation for robotic pruning, flower thinning, and
fruitlet thinning to achieve desired yield and quality.Using color and depth
information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based
instance segmentation technique was developed to identify trunks and branches
of apple trees during the dormant season. Principal Component Analysis was
applied to estimate branch diameter (used to calculate limb cross-sectional
area, or LCSA) and orientation. The estimated branch diameter was utilized to
calculate LCSA, which served as an input for crop-load estimation, with larger
LCSA values indicating a higher potential fruit-bearing capacity.RMSE for
branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95.
Based on commercial apple orchard management practices, the target crop-load
(number of fruit) for each segmented branch was estimated with a mean absolute
error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study
demonstrated a promising workflow with high performance in identifying trunks
and branches of apple trees in dynamic commercial orchard environments and
integrating farm management practices into automated decision-making
Sustainable Fruit Growing
Fruit production has faced many challenges in recent years as society seeks to increase fruit consumption while increasing safety and reducing the harmful effects of intensive farming practices (e.g., pesticides and fertilizers). In the last 50 years, the population has more than doubled and is expected to grow to 9 billion people by 2050. Per capita consumption of fruit is also increasing during this time and the global fruit industry is facing a major challenge to produce enough fruit in quantity and quality. The need for sustainable production of nutritious food is critical for human and environmental health.This book provides some answers to people who are increasingly concerned about the sustainability of fruit production and the fruit industry as a whole
Exploratory Data Analysis on Blueberry yield through Bayes and Function Models
Agricultural researchers are using machine learning to predict crop yield. Many machine learning algorithms need lots of data. One of the major challenges in training and experimenting with machine learning algorithms is the availability of training data in sufficient quality and quantity remains a limiting factor. The Linear Discriminant Analysis produces 95.88% of accuracy which is most efficient of selected models; The Nave Bayes Multinomial has 69.88% accuracy, while the Linear Discriminant Analysis has 0.96 precision. The NBM has 0.71 precision, while Linear Discriminant Analysis has 0.95 recall. The Linear Discriminant Analysis produces 0.99 of ROC, which is the most efficient outcome of selected models. The NBM gives least ROC, which is 0.80. The Linear Discriminant Analysis produces 0.99 of PRC, which is the most efficient outcome of selected models. The NBM gives least PRC, which is 0.72. The LDA explores efficient outcome with low deviations. Four machine-learning-based predictive models were then built using the simulated dataset. This simulated data provides researchers with actual field observation data and those who want to test machine learning algorithms' response to real data with crop yield prediction models
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