Potato Yield Monitoring on Commercial Fields

Davenport, Joan R.; Redulla, C. A.; Hattendorf, M. J.; Evans, Robert G.; Boydston, Rick A.

doi:10.21273/horttech.12.2.289

Cited by 4 publications

(7 citation statements)

References 3 publications

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“…grape crops. As a result, the first load cell yield monitors were targeted into field horticultural crops such as for processing tomatoes and potatoes (Davenport et al, 2002;Demmel & Auernhammer, 1999;Pelletier & Upadhyaya, 1999) with very high yields and a strong difference between the yield and noise signals. Signal processing advances have meant that issues with a low signal to noise ratio can be solved in the electronics and load cell sensors are now effective at even very low off-load flow rates (Taylor et al, 2016).…”

Section: Direct Measurement Principlesmentioning

confidence: 99%

Yield sensing technologies for perennial and annual horticultural crops: a review

et al. 2022

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Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.

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Section: Direct Measurement Principlesmentioning

confidence: 99%

Yield sensing technologies for perennial and annual horticultural crops: a review

et al. 2022

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“…In addition to a separate analysis of different years, growth stages, and cultivars, the small plot data from different years were also pooled together, of which 75% was used for model calibration and 25% for validation. The agreement between the observed and predicted parameters was evaluated using the coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute error (MAE), and relative absolute error (RAE) in prediction, as shown in Equations ( 4)- (7). The models with the largest R 2 and the lowest RMSE (t ha −1 ), MAE (t ha −1 ), and RAE (%) in prediction were identified.…”

Section: Vegetation Index Abbreviation Formula Referencementioning

confidence: 99%

“…Currently, available potato yield monitors are less accessible and the potato yield maps generated from existing yield monitors show low accuracy and inconsistency, resulting in incorrect interpretation of on-farm yield variability [6]. The major influencing factors for the inaccuracy of the potato yield maps produced by the yield monitors include yield sensor calibration, mud, clods or rock separation, operating errors, and data post-processing or cleaning [7]. As a result, yield monitor applications on potato farms are limited.…”

Section: Introductionmentioning

confidence: 99%

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Miao

Rosen

et al. 2021

Remote Sensing

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Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

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“…It seems to be an ideal crop for site-specific crop management and studies into precision potato production have been published since the mid-1990s (Schneider et al 1997 and1996;Hess et al 1998;Persson 1998); however adoption of precision agriculture into potato systems has been slow compared to other annual arable systems. There are multiple potential reasons for this including (among others) a typical 1 in 6 (or greater) rotation for pest and disease considerations in the UK 1 that generates a discontinuity in data collection, the lack of a robust yield monitoring system for potato harvesters (Davenport et al, 2002), the slow development of variablerate potato specific machinery (Kempenaar et al 2018), a lack of a spatial decision support structure for potatoes, a current low ability to vary irrigation spatially in fields, and historically good profit margins that mask production inefficiencies. A change in this last reason, with decreasing profit margins within the UK potato industry over the past decade, and improving agri-technologies has led to renewed interest in site-specific potato management to improve production efficiencies.…”

Section: Introductionmentioning

confidence: 99%

Investigations into the opportunity for spatial management of the quality and quantity of production in UK potato systems

Taylor

Chen

Smallwood³

et al. 2018

Field Crops Research

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Effectively adopting site-specific crop management is dependent on there being production variation within the system. There has been very little published material on yield variation in potatoes and almost none on tuber quality. A review of literature and a systematic survey of 13 potato fields in the UK was performed to better understand within-field variability in potato quantity and quality attributes, including stem density, total yield, marketable yield and tuber size distribution. Non-spatial and spatial statistical analysis of manual and sensor observations was performed on the survey data. Ware production fields exhibited more structured spatial variation in quality attributes (tuber size distribution and % marketable yield) rather than yield attributes. Seed production systems were inversed, with more structured spatial variability in yield attributes than quality attributes. Manual and sensor yield data exhibited a similar level of spatial variation, however the spatially denser sensor data indicated a nested effect with a large amount of short-range variation that may be management related. Both quality and quantity parameters showed sufficient magnitude and range in variation for site-specific management to be considered. The results presented here provide a baseline for spatial variation in both potato quantity and quality to inform future work, particularly for work considering spatial management or spatial modelling in potatoes.

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Potato Yield Monitoring on Commercial Fields

Cited by 4 publications

References 3 publications

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield sensing technologies for perennial and annual horticultural crops: a review

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Investigations into the opportunity for spatial management of the quality and quantity of production in UK potato systems

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