TECHNOLOGIES AND CONSTRUCTIVE SOLUTIONS REGARDING THE INTER-ROW MANAGEMENT OF VINEYARD AND FRUIT TREES

INMATEH Agricultural Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 848 - 860

Published: March 31, 2024

The management of inter-row space vineyards and fruit trees has emerged as an essential approach in sustainable agriculture, optimizing resource use improving ecosystem services. This paper reviews a range innovative technologies solutions aimed at revolutionizing line practices. Modern sensing monitoring systems provide real-time data on soil moisture, nutrient levels, plant health, facilitating precision row-to-row management. Furthermore, techniques for grassing the between rows vines are important management, ensuring good air circulation agricultural activities such maintenance harvesting. In addition, advent seeding machines simplified implementation cover crops. These advanced seed delivery mechanisms, precisely distributing into spaces rows. not only encourages health erosion prevention but also mitigates weed competition, increasing overall resilience agroecosystem. purpose this review is to discuss combination state-of-the-art 3D LIDAR technology, intelligent used trees, solar panel systems, all these examples have revolutionized orchards. holistic optimizes allocation, improves practices, paving way greener more resilient modern agroecosystems.

Language: Английский

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UAV-based individual plant detection and geometric parameter extraction in vineyards

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Nov. 14, 2023

Accurately characterizing vineyard parameters is crucial for precise management and breeding purposes. Various macroscopic are required to make informed decisions, such as pesticide application, defoliation strategies, determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data detect trunk positions extract characteristics, including plant height, canopy width, volume. Our relies solely on geometric features compatible with different training systems collected using various 3D sensors. To evaluate the effectiveness robustness of our proposed approach, conducted extensive experiments multiple grapevine rows trained two systems. method provides more comprehensive characteristics than traditional manual measurements, which not representative throughout row. The experimental results demonstrate accuracy efficiency extracting vital providing valuable insights yield monitoring, grape quality optimization, strategic interventions enhance productivity sustainability.

Language: Английский

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2023, Volume and Issue: unknown

Published: July 6, 2023

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s quality, production, economic prosperity. Fruit grading, particularly the surface fault identification fruit, is indicator export market. This important mangoes, which quite well-liked inBangladesh. On other hand, physical mangoes procedure that labor-intensive, prone to error, very subjective. In this paper, we proposed YOLOv7 integrated Discrete wave transformation computer vision system. The model includes support vector (SVM) decision tree classification high-quality mangoes. results experiments show solution obtained 96.25% accuracy when system was trained tested using publicly accessible mango database.

Language: Английский

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PROSPECTS FOR THE DEVELOPMENT OF DIGITAL MODELS OF GRAPE VARIETIES FOR PREDICTING THE EFFECTIVENESS OF TECHNOLOGICAL PROCESSES

Fruit growing and viticulture of South Russia, Journal Year: 2024, Volume and Issue: 1(85), P. 157 - 173

Published: Jan. 25, 2024

At the present stage, with an increase in volume of consumption grape-growing products, it is necessary to carry out a monitoring forecast possibility its production for each individual variety or scion-rootstock combination, depending on edaphoclimatic conditions and cultivation technology. This possible only if predictive models behavior grape combination are developed grafted culture various ecoagrobiocenoses. The purpose study was consider methodological approaches creation mathematical predicting groups varieties, abiotic agrotechnological characteristics cultivation. To achieve this goal, previously created database used, obtained during experiment conducted basis uterine plantations open school Institute "Agrotechnological Academy" V.I. Vernadsky Crimean Federal University, collected period from 2018 2021 subjected multidimensional regression analysis using program. total number items included 1,860. (31 parameters). research proved developing productivity nonparametric digital introduction as well environmental factors. It established that characterizing quality vine, taking into account varietal weather conditions, can vary particular variety. Thus, similar model Cabernet Sauvignon fundamental multiple correlation coefficient R = 0.9866, Syrah logarithmic at R= 1.0000. Promising possibilities ways (mathematical) varieties by origin according their productivity, technology, parameters manufactured products considered

Language: Английский

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Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(8), P. 3264 - 3264

Published: April 12, 2024

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on automated derivation ecosystem structures individual vine parameters, including heights, leaf area index (LAI), surface (LSA), and geographic positioning single plants. For these intricate segmentation processes nuanced UAS-based data acquisition techniques are necessary. The detection vines was based 3D point cloud data, generated at phenological stage which plants were absence foliage. mean distance from derived locations to reference measurements taken with GNSS device 10.7 cm, root square error (RMSE) 1.07. Vine height normalized digital model (nDSM) using photogrammetric showcased strong correlation (R2 = 0.83) real-world measurements. Vines underwent classification through an object-based image (OBIA) framework. process enabled computation plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing speed manual With use uncrewed aerial systems (UAS) equipped optical sensors, dense clouds computed for canopy-related vines. While LAI LSA computations await validation, they underscore technical feasibility obtaining precise geometric morphological datasets UAS-collected paired analysis.

Language: Английский

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