Development of a tomato load/yield monitor

Pelletier, Gilles; Upadhyaya, Shrini K.

doi:10.1016/s0168-1699(99)00025-3

Cited by 43 publications

(18 citation statements)

References 5 publications

(2 reference statements)

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“…Introduction value horticultural crops have also been investigated such as citrus (Zaman and Schuman 2006;Ye et al 2007), olives (López-Granados et al 2004), apples Best and Zamora 2008), grapes (Bramley andHamilton 2003, Taylor 2004;Bramley 2005), cranberries (Pozdnyakova et al 2005) and tomatoes (Pelletier and Upadhyaya 1999). It is often claimed that precision agriculture can offer a great deal to the production of high value crops, and it is also easier to pay for the investment than for lower value crops.…”

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confidence: 99%

Spatial variation in yield and quality in a small apple orchard

et al. 2009

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We describe the yield and quality of apples from a 0.8 ha apple orchard located in northern Greece over two growing seasons and consider the potential for site-specific management. The orchard has two apple cultivars: Red Chief (main cultivar) and Fuji (pollinator). Yield was measured by weighing all fruit harvested from groups of five adjacent trees and the position of the central tree was recorded by GPS. Apple quality at harvest was evaluated from samples of the two cultivars in both years for which fruit mass, flesh firmness, soluble solids content, juice pH and acidity of the juice were determined. The variation in tree flowering was also measured in the spring of the second season using a stereological sampling procedure. The results showed considerable variability in the number of tree flowers, yield and quality across the orchard for both cultivars. The number of flowers was strongly correlated with the final yield. These data could potentially be used to plan precise thinning and for early prediction of yield; the latter is important for marketing the fruit. Several quality characteristics, including fruit juice soluble solids content and acid content were negatively correlated with yield. The general patterns of spatial variation in several variables suggested that changes in topography and aspect had important effects on apple yield and quality.

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confidence: 99%

Spatial variation in yield and quality in a small apple orchard

et al. 2009

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“…Precision agriculture is a knowledge-based management technique that can empower growers to apply accurate management practices in irrigation, application of agro-chemicals, scheduling of grove equipment and pickers, and more. Yield monitoring is one of the main components of precision agriculture (Pelletier and Upadhyaya 1999;Rosa et al 2000). Yield monitoring systems are commercially available for grains; however, very few yield monitoring systems are commercially available for fruit and vegetable crops.…”

Section: Introductionmentioning

confidence: 99%

“…The total field error in the weight measurement was reported to be less than 1%. Pelletier and Upadhyaya (1999) developed a continuous tomato yield measuring and mapping system. It consisted of a three-idler weigh bridge, an angle transducer, a belt speed sensor and a DGPS receiver to generate yield maps.…”

Section: Introductionmentioning

confidence: 99%

Development of a yield monitoring system for citrus mechanical harvesting machines

Maja

Ehsani

2009

Precision Agric

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A load cell based yield monitoring system was developed for the Oxbo citrus mechanical harvesting machines. The yield monitoring system consisted of a GPS receiver, a mass flow sensor and data processing and storage units. The mass flow sensor consisted of four load cells attached to a carbon-fiber plate which sensed the impact force created by the oranges hitting the plate. A mathematical model was developed to relate the impact force to fruit mass. Laboratory tests were conducted on a test rig that replicated the flow of oranges to measure the accuracy of the system under a controlled environment. The system performed very well under laboratory conditions (R 2 = 0.99 and an average error of 3.3%). In addition, a field test was conducted in a citrus orchard in Florida to evaluate the performance of the system under field conditions. Of the 72 rows used in the field test, the first 10 rows were used to calibrate the computed weight. A correlation of R 2 = 0.97 between the actual weight and the computed weight was found from the field data with an average error of 7.81%.

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“…It is well known that in order to perform a yield variability study of other sources of variation, broad data registers are required. Yield mapping systems for grains (Triticum aestivum L., Hordeum vulgare L. and Zea mays L.) have been commercially available for over a decade (Pelletier and Upadhyaya, 1999). Also, several systems and methodologies have been developed for non Spanish Journal of Agricultural Research (2006) 4(2), 130-139 combinable crops, including: a non-grain crops yield sensing system developed using the measurement of mass accumulation rate (Godwin and Wheeler, 1997); a sugar beet (Beta vulgaris L. var.…”

Section: Introductionmentioning

confidence: 99%

“…Also, several systems and methodologies have been developed for non Spanish Journal of Agricultural Research (2006) 4(2), 130-139 combinable crops, including: a non-grain crops yield sensing system developed using the measurement of mass accumulation rate (Godwin and Wheeler, 1997); a sugar beet (Beta vulgaris L. var. saccharifera Alef) yield sensing system based on flow rate, developed in the US (Hofman et al, 1995); a continuous mass flow yield monitor for tomato developed in the US (Pelletier and Upadhyaya, 1999). However, there are many other crops, which have not yet been involved in the concept of 'precision farming'.…”

Section: Introductionmentioning

confidence: 99%

Yield mapping system for vegetables picked up with a tractor-pulled platform

Saldaña

Cabrera

Serwatowski

et al. 2006

Span. j. agric. res.

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The aim of this work was to develop a yield mapping system for vegetables. A harvest assistance equipment was modified and instrumented to obtain yield maps. Instrumentation consisted in the implementation of a mass accumulation rate measurement system, which was used to determine weight of harvested broccoli (Brassica oleracea var. italica Plenck), a sensor of distance travelled to determine harvested area and a Global Positioning System (GPS) to determine position of harvested area within the field. Results found in evaluation purpose tests of the yield mapping system showed an error of less than 2.69% in the weight data and 1.49% in the area data, whereas in yield mapping tests, the system worked properly without interfering with the harvesting methodology employed for field workers. Also, the comparison of yield maps obtained with the two GPSs of different precision, confirmed that the less precise and lower cost GPS can be used to map yield. In the future, the yield mapping system can be used to assess the causes of yield variability and field workers performance.Additional key words: geo-referenced data, harvest, low cost GPS, precision farming. ResumenSistema de mapeo de rendimiento de cosecha de hortalizas recolectadas con un remolque tirado por tractor El principal objetivo de este trabajo fue desarrollar un sistema de mapeo de rendimiento para hortalizas. Un equipo de asistencia a la recolección de bróculi ha sido modificado e instrumentado para obtener mapas de rendimiento. Se han instalado y verificado la siguiente instrumentación: sensores de carga para determinación del peso de bróculi cosechado; sensor de distancia recorrida para determinar el área cosechada y sistema de posicionamiento global (GPS) para georreferenciar el área cosechada en campo. Los resultados de las pruebas realizadas para evaluar los montajes y procedimientos de medida mostraron errores inferiores al 2,69% en las mediciones de peso y menores al 1,49% en las mediciones del área. Los resultados obtenidos en las pruebas de verificación de mapeo del rendimiento -localización de sus valores-fueron asimismo satisfactorios. Por último, se valida el uso de un GPS de bajo costo, para mapear la variabilidad del rendimiento de cosecha en campos de hortalizas. En el futuro, el sistema de mapeo de rendimiento puede ser empleado para identificar las causas de variación del rendimiento y el desempeño de los trabajadores en el campo.Palabras clave adicionales: agricultura de precisión, cosecha, datos georreferenciados, GPS de bajo costo.

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Development of a tomato load/yield monitor

Cited by 43 publications

References 5 publications

Spatial variation in yield and quality in a small apple orchard

Spatial variation in yield and quality in a small apple orchard

Development of a yield monitoring system for citrus mechanical harvesting machines

Yield mapping system for vegetables picked up with a tractor-pulled platform

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