Detection of coffee fruits on tree branches using computer vision

Article Agriculture, Multidisciplinary

Recognition of sweet peppers and planning the robotic picking sequence in high-density orchards

Zhengtong Ning et al.

Summary: This study proposes an algorithm for recognizing sweet peppers and planning a picking sequence in an efficient and collision-free manner for robot picking in complex orchards. The algorithm combines the you only look once model with a convolutional block attention module for sweet pepper recognition, and improves the clustering algorithm for fast search-and-find of density peaks. The results show improved accuracy and success rates, reduced collision damage, and increased picking efficiency compared to existing methods.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Computer Science, Artificial Intelligence

Improved YOLO object detection algorithm to detect ripe pineapple phase

Nguyen Ha Huy Cuong et al.

Summary: This study improves a computational model for detecting pineapple ripening using a deep learning classifier, aiming to increase agricultural productivity. With a large dataset and the use of mobile phone cameras, researchers are able to accurately identify and monitor pineapple maturity.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2022)

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Article Horticulture

Obtaining and Validating High-Density Coffee Yield Data

Mauricio Martello et al.

Summary: This study evaluates the quality of yield data obtained through a yield monitor onboard a coffee harvester and finds a high correlation with data collected using traditional measurement methods. Additionally, by collecting data over three consecutive seasons, the study identifies the internal variability of coffee yield and categorizes regions based on alternating yield patterns between years. The findings suggest that, in order to make effective management decisions, both spatial and biennial yield variability should be taken into account.

HORTICULTURAE (2022)

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Article Agriculture, Multidisciplinary

Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions

Hamzeh Mirhaji et al.

Summary: Fruit load estimation is crucial for Precision Agriculture. The study applied YOLO detection models to detect and count ripe oranges in an orchard, achieving high performance and accuracy through transfer learning and RGB image training. The YOLO-V4 model showed promising results in detecting and estimating orange yield, with high precision and performance.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Agriculture, Multidisciplinary

Detection, classification, and mapping of coffee fruits during harvest with computer vision

Helizani Couto Bazame et al.

Summary: This study implemented an algorithm with a computer vision model to detect and classify coffee fruits, assigning geographic coordinates to each frame for mapping detection summaries. The model used Darknet and YOLOv3-tiny for fruit detection and classification, achieving high precision on the validation set.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Robotics