A comparison of canopy and soil proximal sensing to implement selective harvesting in viticulture

2021 ◽  
pp. 157-164
Author(s):  
D. Sarri ◽  
S. Priori ◽  
R. Lisci ◽  
S. Lombardo ◽  
L. D’Avino ◽  
...  
Keyword(s):  
Proximal Sensing ◽  
Selective Harvesting

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

scholarly journals Selective Harvesting Robotics: Current Research, Trends, and Future Directions

2021 ◽  
Author(s):  
Gert Kootstra ◽  
Xin Wang ◽  
Pieter M. Blok ◽  
Jochen Hemming ◽  
Eldert van Henten
Keyword(s):  
Review Paper ◽  
Production Systems ◽  
Future Research ◽  
Limiting Factor ◽  
Future Directions ◽  
Research Directions ◽  
Selective Harvesting ◽  
Important Solution ◽  
Future Research Directions ◽  
Current Systems

Abstract Purpose of Review The world-wide demand for agricultural products is rapidly growing. However, despite the growing population, labor shortage becomes a limiting factor for agricultural production. Further automation of agriculture is an important solution to tackle these challenges. Recent Findings Selective harvesting of high-value crops, such as apples, tomatoes, and broccoli, is currently mainly performed by humans, rendering it one of the most labor-intensive and expensive agricultural tasks. This explains the large interest in the development of selective harvesting robots. Selective harvesting, however, is a challenging task for a robot, due to the high levels of variation and incomplete information, as well as safety. Summary This review paper provides an overview of the state of the art in selective harvesting robotics in three different production systems; greenhouse, orchard, and open field. The limitations of current systems are discussed, and future research directions are proposed.

scholarly journals Ultraviolet and Near‐Infrared Dual‐Band Selective‐Harvesting Transparent Luminescent Solar Concentrators

2021 ◽  
Vol 11 (12) ◽  
pp. 2003581
Author(s):  
Chenchen Yang ◽  
Wei Sheng ◽  
Mehdi Moemeni ◽  
Matthew Bates ◽  
Christopher K. Herrera ◽  
...  
Keyword(s):  
Near Infrared ◽  
Dual Band ◽  
Solar Concentrators ◽  
Selective Harvesting ◽  
Luminescent Solar Concentrators

scholarly journals Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

2017 ◽  
Vol 51 (10) ◽  
pp. 5630-5641 ◽  
Author(s):  
Raphael A. Viscarra Rossel ◽  
Craig R. Lobsey ◽  
Chris Sharman ◽  
Paul Flick ◽  
Gordon McLachlan
Keyword(s):  
Soil Organic C ◽  
Proximal Sensing ◽  
Organic C ◽  
C Stocks

scholarly journals Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1409
Author(s):  
Nicholas Todd Anderson ◽  
Kerry Brian Walsh ◽  
Dvoralai Wulfsohn
Keyword(s):  
Machine Vision ◽  
Structural Parameters ◽  
Qualitative Assessment ◽  
Sampling Strategies ◽  
Yield Data ◽  
Proximal Sensing ◽  
Fruit Number ◽  
Fruit Maturity ◽  
Harvest Timing ◽  
Fruit Load

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

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