Sensors and Remote Sensing in Precision Horticulture
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 3043
Special Issue Editors
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Precision horticulture is a data-driven management method that collects site- or plant-specific information of fruits and vegetables in order to (1) make in-growth decisions to improve production and (2) postharvest process management. Precision horticulture is particularly advantageous to the farmer due to the high value of their products and the high quantities of crop inputs required to produce horticultural crops. Clearly any cost reduction significantly boosts producer profits and effective utilization of crop inputs may lessen the environmental impact of horticultural crop production.
In horticulture, analysis of the product's quality is more crucial than in any other crop. Typically, the field size is less than that of agricultural output. Even single plants may be handled individually in accordance with the spatial or temporal pattern, as the planting density is reduced. Precision horticulture implementation relies primarily on sensors and systems that can collect weather, soil, and plant-specific data at a reasonable cost. Optical sensors are the most prevalent, and many approaches have demonstrated the promise for effective, quick, non-invasive in-situ disease diagnosis and yield estimate. The most common applications are biotic and abiotic stress detection at asymptomatic or early stages, canopy size and density, yield estimation, and crop quality, among other data.
Dr. Alessandro Matese
Dr. Santhana Krishnan Boopalan
Guest Editors
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Keywords
- precision horticulture
- yield monitor
- quality monitor
- agricultural decision support systems (AgriDSS)
- remote sensing applications
- proximal sensors
- artificial intelligence (AI) and machine learning (ML) methodologies
- internet of Things (IoT)
- variable-rate input applications
- automated machinery and agricultural robots
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