Snap beans and kidney beans are poor nitrogen fixers and need nitrogen (N) fertilizer.However, excessive application of N leads to groundwater contamination.The traditional way of measuring crop nitrogen status is destructive and time-consuming.The objective of this study was to develop a tool that accurately predict the real-time crop nitrogen status and the end-of season yield for optimizing fertilizer management.The field trial was conducted in 2022 and 2023.Eight nitrogen treatments were applied at 22 kg ha -1 , 56 kg ha -1 , 84 kg ha -1 , 112 kg ha -1 , 140 kg ha -1 , 168 kg ha-1, 196 kg ha -1 , 224 kg ha -1 to three kidney beans cultivars.Six nitrogen treatments were applied at 22 kg ha -1 , 56 kg ha -1 , 84 kg ha -1 , 112 kg ha -1 , 140 kg ha -1 , 168 kg ha -1 to two snap beans cultivars.Hyperspectral images (400 nm to 2500 nm) were collected on a weekly basis.Top twenty bands along with genotype (cultivars), environmental factors (temperature, precipitation, growing Degree Days), management factors (nitrogen rate, days after planting, irrigation) were used to train different machine learning algorithms including linear regression, random forest, XG Boost, support vector machine and k-nearest neighbors for predicting the nitrogen status and the final yield.Our results indicated that top twenty bands along with GEM performed the best for predicting final yield (R 2 as high as 0.82 and RMSE as low as 1.6).Our study demonstrated the potential capacity of hyperspectral imaging and machine learning models to estimate crop yield and nitrogen status.
Snap beans and kidney beans are poor nitrogen fixers and need nitrogen (N) fertilizer. However, excessive application of N leads to groundwater contamination. The traditional way of measuring crop nitrogen status is destructive and time-consuming. The objective of this study was to develop a tool that accurately predict the real-time crop nitrogen status and the end-of season yield for optimizing fertilizer management. The field trial was conducted in 2022 and 2023. Eight nitrogen treatments were applied at 22 kg ha, 56 kg ha, 84 kg ha, 112 kg ha, 140 kg ha, 168 kg ha-1, 196 kg ha, 224 kg ha to three kidney beans cultivars. Six nitrogen treatments were applied at 22 kg ha, 56 kg ha, 84 kg ha, 112 kg ha, 140 kg ha, 168 kg ha to two snap beans cultivars. Hyperspectral images (400 nm to 2500 nm) were collected on a weekly basis. Top twenty bands along with genotype (cultivars), environmental factors (temperature, precipitation, growing Degree Days), management factors (nitrogen rate, days after planting, irrigation) were used to train different machine learning algorithms including linear regression, random forest, XG Boost, support vector machine and k-nearest neighbors for predicting the nitrogen status and the final yield. Our results indicated that top twenty bands along with GEM performed the best for predicting final yield (R as high as 0.82 and RMSE as low as 1.6). Our study demonstrated the potential capacity of hyperspectral imaging and machine learning models to estimate crop yield and nitrogen status.
Proper monitoring of plant nitrogen (N) status and yield forecasting is essential to achieving a healthy crop and to maximizing profitability, especially in N-demanding crops such as potato. The most common method of monitoring potato N status (nitrate-N analysis of petioles) by the potato farmers in Wisconsin is time-consuming, destructive, and is impractical to sufficiently characterize spatial-temporal variability. This study utilized narrow-band hyperspectral imagery (including the visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral regions) collected over two growing seasons from two potato varieties (Russet Burbank and Soraya) grown under varied N treatments to develop robust partial least squares regression (PLSR) models for predicting potato in-season and at-harvest traits related to N. The results indicate that some traits such as leaf total N content, within-season tuber yield, and the marketable yield and quality at harvest could be well predicted for both varieties ( R 2 up to 0.78). The best spectral regions for those predictions varied depending on the growth stages of the plants, with VNIR predominating during early and mid-tuber, and SWIR during late tuber bulking. Our research suggests that the narrow-band hyperspectral imaging technique could be utilized to develop robust models to assist and potentially improve crop N fertilization decision-making, which will eventually result in higher input use efficiency of the cropping systems and better environmental stewardship for the farmers.
Irrigation is required for profitable commercial potato (Solanum tuberosum L.) production. Excessive or deficit soil water availability during the growing season can have adverse effects on tuber yield, quality, and storability. A field study was conducted during the 2018 and 2019 field and storage seasons in Central Wisconsin, a region in the U.S. with a high volume of potato production, to evaluate the impacts of different irrigation rates on three chipping potato varieties, Hodag, Lamoka, and Snowden. The treatments were implemented during the late-tuber bulking and tuber maturation growth stages, and consisted of irrigation at 125%, 100%, 75%, and 50% of crop evapotranspiration (ET). Irrigation before the treatment period was at 100%ET for all plots. With the industry standard irrigation practice being at 100%ET, other treatments were designated as over-irrigation or deficit irrigation. The impact of these watering rates on tuber yield and quality was evaluated at harvest, and tuber storage quality was assessed by measuring chip fry color and sugar concentrations at 0, 4, and 8 months of storage. It was found that compared to the standard practice, the over-irrigation treatment at 125%ET when tubers reached late bulking resulted in no significant increase in total yield, marketable yield, tuber quality at harvest and during storage, as well as reduced irrigation efficiency (IE) and water-use efficiency (WUE). This treatment also increased nitrate leaching potential in both years. In comparison, deficit irrigation at 75%ET or even 50%ET during the late season had no impact on tuber growth, could increase IE and WUE in one of the two years, and showed reduced drainage. In both years, irrigation rate had no significant effects on hollow heart incidence, tuber specific gravity at harvest, and fry quality during the 8-month storage period. This study suggested that over-irrigation was not beneficial for potato production in Central Wisconsin of the U.S., and deficit irrigation during late tuber bulking and tuber maturation stages could potentially result in more sustainable water use while not penalizing tuber yield, quality and storability of chipping potatoes.
In order to understand the similarity among burley tobacco germplasm,149 burley tobacco cultivars from China and abroad were systematically clustered by cluster software NTSYS 2.10e based on data of 19 traits.Results showed that all burley tobacco cultivars can be clustered into 6 large cultivar groups and 2 small cultivar subgroups at dissimilitude coefficient of 0.0375.Group I to group VI included 25,22,23,18,24,and 37 cultivars,respectively.Total coefficient of all burley tobacco cultivars ranged from 0.01 to 0.53,indicating that there existed some differences among various germplasm.
ABSTRACT Processed potato ( Solanum tuberosum L.) products, such as chips and French fries, contribute to the dietary intake of acrylamide, a suspected human carcinogen. One of the most promising approaches for reducing its consumption is to develop and commercialize new potato varieties with low acrylamide‐forming potential. To facilitate this effort, a National Fry Processing Trial (NFPT) was conducted from 2011 to 2013 in five states. More than 140 advanced breeding lines were evaluated for tuber agronomic traits and biochemical properties from harvest through 8 mo of storage. Thirty‐eight and 29 entries had significantly less acrylamide in French fries than standard varieties Russet Burbank and Ranger Russet, with reductions in excess of 50%, after one and 8 mo of storage, respectively. As in previous studies, the glucose content of raw tubers was predictive of acrylamide in finished French fries ( R 2 = 0.64–0.77). Despite its role in acrylamide formation, tuber free asparagine was not significant, potentially because it showed relatively little variation in the NFPT population. Even when glucose was included in the model as a covariate, genotype was highly significant ( p = 0.001) for predicting acrylamide, indicating there may be yet‐unidentified genetic loci to target in breeding. The NFPT has demonstrated that there exist many elite US breeding lines with low acrylamide‐forming potential. Our ongoing challenge is to combine this trait with complex quality attributes required by the fry processing industry.
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