About
20
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Introduction
Jordi Gené-Mola received the B.S. degree in Mechanical Engineering, the M.Sc. degree in Industrial Engineering and the PhD degree from the Universitat de Lleida, in 2013, 2015 and 2020 respectively, and the M.Sc. degree in Computer Vision from the Universitat Autònoma de Barcelona, in 2018. He is currently working as postdoc researcher in the Research Group in Precision Agriculture (GRAP), University of Lleida. His research interests include the use of sensors and computer vision in agriculture.
Additional affiliations
January 2022 - present
June 2019 - September 2019
March 2016 - May 2020
Education
October 2017 - September 2018
March 2016 - May 2020
September 2013 - June 2015
Publications
Publications (20)
The emergence of low-cost 3D sensors, and particularly RGB-D cameras, together with recent advances in artificial intelligence, is currently driving the development of in-field methods for fruit detection, size measurement and yield estimation. However, as the performance of these methods depends on the availability of quality fruit datasets, the d...
This paper describes a new robot specifically developed for topiary trimming and evaluates its performance through a novel evaluation method. Experiments were carried out in a real garden letting the robot trim spherical-, cylindrical- and cuboid-shaped boxwood topiaries. The robot's performance was evaluated using a quantitative evaluation method,...
The use of three-dimensional registration techniques is an important component for sensor-based localization and mapping. Several approaches have been proposed to align three-dimensional data, obtaining meaningful results in structured scenarios. However, the increased use of high-frame-rate 3D sensors has lead to more challenging application scena...
The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees (Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees conta...
In-field fruit monitoring at different growth stages provides important information for farmers. Recent advances have focused on the detection and location of fruits, although the development of accurate fruit size estimation systems is still a challenge that requires further attention. This work proposes a novel methodology for automatic in-field...
Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous dr...
The use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivi...
The present dataset contains colour images acquired in a commercial Fuji apple orchard (Malus domestica Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled “Fruit detection and 3D location using instance segmentati...
Currently, 3D point clouds are obtained via LiDAR (Light Detection and Ranging) sensors to compute vegetation parameters to enhance agricultural operations. However, such a point cloud is intrinsically dependent on the GNSS (global navigation satellite system) antenna used to have absolute positioning of the sensor within the grove. Therefore, the...
The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work pres...
This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an...
Yield monitoring and geometric characterization of crops provide information about orchard variability and vigor, enabling the farmer to make faster and better decisions in tasks such as irrigation, fertilization, pruning, among others. When using LiDAR technology for fruit detection, fruit occlusions are likely to occur leading to an underestimati...
The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimised harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions....
Yield prediction provides valuable information to plan the harvest campaign, fruit storage and sales. Traditionally, yield estimation has been carried out by manual counting of randomly selected samples, without addressing spatial variability within the orchard. To obtain a precise estimation it is necessary to sample a relatively large number of t...
This article contains data related to the research article entitle “Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities” [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown pot...
Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time...
Agricultural aerosol emissions can significantly impact human and animal health as well as the environment. Therefore, it is essential to adopt new sensing techniques for real-time monitoring these emissions in high temporal and spatial resolution. In recent years, light detection and ranging (lidar) technology has been used for measuring the parti...
Projects
Projects (3)
1. Fruit detection and 3D modeling based on the combined use of photonic-based sensors and computer vision.
2. Sampling schemes for the efficient estimation of the harvest in fruit plantations.
The PAgFRUIT project (http://www.pagfruit.udl.cat/en/, RTI2018-094222-B-I00, MCIU) addresses four issues to improve fruit orchard management: (1) Canopy phenotyping by means of photon-based sensors (LiDAR, RGB, RGB-D cameras, multispectral sensors) to monitor vegetation and to decide about mechanical pruning methods adapted to special constraints to enhance yield, (2) Early detection of pests and diseases by means of remote sensing from drones, and crop protection methods adapted to spatially variable plots to optimize pesticide dosage, (3) Fruit detection and 3D modelling based on the combined use of photon-based sensors and computer vision and (4) Efficient sampling methods for yield estimates adapted to effective scouting within the plots. Under this approach, the project has been methodologically designed in two main objectives: (1) Canopy phenotyping and spatial variability evaluation in fruit and vine orchards, and (2) Practical application of canopy parameters and spatial information in pruning management, precise pesticide application, and efficient scouting and sampling for fruit orchards monitoring and yield estimation. The project aims to continue previous work on Precision and Sustainable Fruit Production of the Research Group on AgroICT and Precision Agriculture (GRAP, University of Lleida), to which the major part of the research team and of the working plan belong.
Photonic-based tools for a sustainable agronomic management and use of pesticides in tree crops in the framework of precision farming
