Évolution de la teneur en eau des grains et détermination de la maturité physiologique du maïs-grain (<i>Zea mays</i> L.)

Tremblay, G L; Filion, P.; Tremblay, Marc Adélard; Bérard, M.; Durand, Jean Louis; Goulet, Jean; Montpetit, J. M.

doi:10.4141/cjps07058

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The MC in the harvested grains ranged from 25.9-31.0%, and the impact of MC on grain weight varied between cultivars. The range of grain MC measured here was consistent with previous reports of grain MC in many maize hybrids during the PM period [12,31]. The differences in grain weight between hybrids were not affected by their maturation periods [14,52].…”

Section: Discussionsupporting

confidence: 90%

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Dynamics of Maize Grain Weight and Quality during Field Dehydration and Delayed Harvesting

Zhao

Wang

et al. 2023

Agriculture

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Dehydration of maize grains in the field can reduce costs associated with drying after harvest. A delayed harvest approach after physiological maturity, in which plants stand in the field to allow the stems to dry, has been widely adopted in maize production. However, it remains unclear how harvesting at different time points during the dehydration stage may affect grain yield and quality. In the present study, experiments were conducted in the Ningxia Irrigation Area of northwest China from 2019 to 2022, we continuously observed and used a linear-plateau model to analyze the changes in grain weight and quality traits (such as bulk density and levels of starch, protein, oil, fiber, and free fatty acids) during the field dehydration period of maize hybrids with differing maturity times. Harvesting at a grain moisture content of >31.0% was shown to affect grain weight, whereas harvesting at a moisture content of <25.9% did not affect grain weight or yield. The stable period for grain weight occurred during the physiological maturity stage for an early-maturing hybrid and 5–12 days before physiological maturity for the mid–late-maturing hybrids. When the field grain moisture content was <37%, harvesting did not affect the grain bulk density. Grain bulk density tended to stabilize one to two weeks earlier than grain weight and two to three weeks before the physiological maturity period. The protein, oil, fiber, and free fatty acid contents in maize kernels at 30 days after silking were not affected by the harvesting period, and the starch contents were unaffected in maize kernels harvested at any time later than 50 days after silking. Overall, maize grain should be harvested during field dehydration and delayed harvesting after physiological maturity with relatively low moisture content to get a better yield with superior quality.; delayed harvesting is therefore an important technical approach to improve the efficient production of high-quality maize.

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Section: Discussionsupporting

confidence: 90%

“…The Harvest Quality Report by the US Food Commission showed that the average grain MC was 16.41% during the maize harvest period from 2011 to 2019 [18]. Maize MC during the PM period typically ranges from 15-42%, with significant differences between plants with different genetic backgrounds and growth environments [8,31].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Maize Grain Weight and Quality during Field Dehydration and Delayed Harvesting

Zhao

Wang

et al. 2023

Agriculture

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“…Previous researchers have attempted to define a specific kernel moisture value for determining physiological maturity. However, it has been reported that the kernel moisture content at physiological maturity (black layer maturity) ranges from 15 to 42% and differs significantly with the hybrid and the environment (Carter & Poneleit, 1973;Daynard, 1972;Li et al, 2017;Rench & Shaw, 1971;Tremblay et al, 2008). This range of values for kernel moisture content might be due to the black layer method.…”

Section: Crop Sciencementioning

confidence: 99%

The stability and variability of maize kernel moisture content at physiological maturity

Ming

Wang

et al. 2020

Crop Science

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Maize (Zea mays L.) kernel moisture content at physiological maturity significantly differs among hybrids and is affected by the environment. Nonetheless, 35% was previously reported and is widely accepted as the moisture content at physiological maturity. To verify whether the 35% moisture content could be applied to various hybrids and regions, a multi‐area, multi‐year trial was conducted from 2015 to 2018 in six locations in different maize‐planting regions of China. Time‐series kernel moisture contents and kernel dry weights were investigated for 156 hybrids in total to ascertain the growth pattern of the percentage maximum kernel dry weight as a function of kernel moisture content. The growth patterns were fitted with a tri‐linear model with a plateau, and at the point at which the percentage maximum kernel dry weight first reached its maximum (physiological maturity), the kernel moisture contents were 32% for summer maize, 34% for spring maize, and 38% for Daqing (a special spring maize region). We found that regional variation existed in kernel moisture content at physiological maturity. The regional average moisture content at physiological maturity could thus be used as an indicator to determine physiological maturity for most hybrids in the region. In addition, varietal differences in moisture content could be accounted for when using the regional average value.

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“…For the second task, which involves determining maturity information based on critical physiological and biochemical traits of crops, the most common approach is the fixed threshold method. For example, some studies define maize grain maturity as a water content less than 35%, while sunflower fruit maturity is considered when the water content is greater than 40% [37,38]. Fiber fineness thresholds have also been employed to determine cotton maturity [39].…”

Section: Introductionmentioning

confidence: 99%

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Hu,

Feng,

Wang

et al. 2024

Remote Sensing

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Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

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Évolution de la teneur en eau des grains et détermination de la maturité physiologique du maïs-grain (Zea mays L.)

Cited by 5 publications

References 12 publications

Dynamics of Maize Grain Weight and Quality during Field Dehydration and Delayed Harvesting

Dynamics of Maize Grain Weight and Quality during Field Dehydration and Delayed Harvesting

The stability and variability of maize kernel moisture content at physiological maturity

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

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