scholarly journals Crop Yield Mapping: Comparison of Yield Monitors and Mapping Techniques

2015 ◽  
pp. 15-31 ◽  
Author(s):  
Stuart J. Birrell ◽  
Steven C. Borgelt ◽  
Kenneth A. Sudduth
Keyword(s):  
Crop Yield ◽  
Yield Mapping ◽  
Mapping Techniques

Two methods for processing yield maps from multiple sensors in large vineyards in California

2017 ◽  
Vol 8 (2) ◽  
pp. 530-533 ◽  
Author(s):  
B. Sams ◽  
C. Litchfield ◽  
L. Sanchez ◽  
N. Dokoozlian
Keyword(s):  
Processing Time ◽  
Fruit Weight ◽  
Wine Grape ◽  
Total Processing Time ◽  
Yield Mapping ◽  
Processing Methods ◽  
Multiple Sensors ◽  
Level Method ◽  
Mapping Techniques ◽  
New Processing

Yield mapping techniques have only recently started to be implemented by the Californian wine grape industry, but the advancement has necessitated new processing methods for large vineyards. The process for mapping large blocks harvested with multiple machines has only recently occurred and implies that their yield monitors have to be calibrated and corrected to the same scale. Here we discuss two methods for processing yield maps at the commercial level. Method 1 depends on many calibrations with delivered fruit weight to a winery. Method 2 normalizes raw files automatically can reduce total processing time by up to 90%.

Comparison of sensors and techniques for crop yield mapping

1996 ◽  
Vol 14 (2-3) ◽  
pp. 215-233 ◽  
Author(s):  
Stuart J. Birrell ◽  
Kenneth A. Sudduth ◽  
Steven C. Borgelt
Keyword(s):  
Crop Yield ◽  
Yield Mapping

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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