Smart Sensors and Smart Data for Precision Agriculture: A Review

Soussi, Abdellatif; Zero, Enrico; Sacile, Roberto; Trinchero, Daniele; Fossa, Marco

doi:10.3390/s24082647

Cited by 8 publications

(1 citation statement)

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“…MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content. assimilation of data from diverse sensors and guaranteeing compatibility between different systems and platforms [6]. Standardization is necessary for data analysis and interoperability across multiple platforms for smooth functioning.…”

Section: Introductionmentioning

confidence: 99%

Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics

Rana,

Gerbino,

Akbari Sekehravani

et al. 2024

Preprint

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Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and orthomosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis). The methodology employs YOLOv8, Grounding DINO, and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using initial datasets, which then facilitated the training of the Grounded SAM framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study's findings highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. Results indicate consistent and comparable growth patterns between aerial images and orthomosaics, with significant periods of rapid expansion and minor fluctuations over time. The integration of these advanced techniques demonstrates the potential for enhancing precision agriculture through reduced labor and increased accuracy in crop growth monitoring.

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Section: Introductionmentioning

confidence: 99%