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[This corrects the article DOI: 10.1371/journal.pone.0124127.].

Foxtail millet is a nutritionally rich and climate-resilient cereal crop; however, poor germination and weak early seedling growth often limit its productivity. This study evaluated the effects of seed priming on germination, seedling vigor, yield attributes, and grain yield of foxtail millet through laboratory and field experiments. In the laboratory study, four foxtail millet varieties were subjected to six priming chemicals at two concentrations each, along with hydropriming and an unprimed control. Seed priming significantly influenced all germination and seedling vigor traits. The highest germination percentage (86.44%) and germination index (116.49), were recorded under NaCl priming at 10000 ppm. Maximum seedling vigor index (6.08), speed of emergence (86.51), germination energy (61.63) were achieved with NaOCl at 500 ppm, while the lowest germination performance occurred under CaCl.sub.2 at 20000 ppm. The shortest time to 50% germination (T.sub.50 = 1.55 days), mean germination time (MGT = 4.54 days) and seedling vigor index were obtained from no priming, while the longest T.sub.50 (2.0 days) and MGT (4.78 days) were recorded with KNO.sub.3 at 30000 ppm. The longest shoot (3.76 cm) and shoot dry weight (28.96 mg) were obtained with KNO.sub.3 at 15000 ppm, while the longest root (3.92 cm) and seedling length (7.51 cm) were recorded under NaOCl at 1000 ppm. The lowest shoot length (2.18 cm), root length (2.08 cm), seedling length (4.26 cm), shoot (12.03 mg), root (10.66 mg) and seedling (22.69 mg) dry weight were obtained from no priming. Based on laboratory performance, selected treatments were evaluated under field conditions. During the winter season, the highest grain (2.72 t ha.sup.-1) and straw (5.03 t ha.sup.-1) were recorded from BARI Kaon-2 x NaCl (10000 ppm). The highest grain yield obtained under this treatment combination was due to the production of the highest values for ear length (17.83 cm), ear weight (14.30 g), filled grains ear.sup.-1 (2918.33) and 1000-grain weight (2.56 g). Whereas, the lowest grain yield (1.17 t ha.sup.-1) was given by BARI Kaon-1 x no priming. In the summer season, the highest grain yield (3.93 t ha.sup.-1) was obtained from BARI Kaon-1 x NaCl (10000 ppm) this was due to the production of the higher values for most of the yield attributes by this treatment combination. In summer season, the lowest grain yield was obtained from BARI Kaon-4 x no priming. In conclusion, BARI Kaon-2 x NaCl (10000 ppm) performed best during the winter season, whereas BARI Kaon-1 x NaCl (10000 ppm) exhibited superior performance during the summer season. Seed priming with NaCl (10000 ppm) emerged as a seasonally robust strategy to improve germination, crop establishment, and yield of foxtail millet in Bangladesh.

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops.

A key concern in agriculture is how to feed the expanding population and safeguard the environment from the ill effects of climate change. To feed a growing global population, food production and security are significant problems, as food output may need to double by 2050. Thus, more innovative and effective approaches for increasing agricultural productivity (hence, food production) are required to meet the rising demand for food. The world’s most widely cultivated grains include corn, wheat, and rice, which serve as the foundation for basic foods. This review focuses on some of the key most up-to-date approaches that boost wheat, rice, corn, barley, and oat yields with insight into how molecular technology and genetics may raise the production and resource-efficient use of these important grains. Although red light management and genetic manipulation show maximal grain yield enhancement, other covered strategies including bacterial-nutrient management, solar brightening, facing abiotic stress through innovative agricultural systems, fertilizer management, harmful gas emissions reduction, photosynthesis enhancement, stress tolerance, disease resistance, and varietal improvement also enhance grain production and increase plant resistance to harmful environmental circumstances. This study also discusses the potential challenges of the addressed approaches and possible future perspectives.

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.

There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.

Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an R 2 of 0.49168, while XGBoost had an MSE of 0.03583 and an R 2 of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an R 2 of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.

Strawberry is a popular functonal food due to the presence of antoxidant and ant-inflammatory phytochemicals. Enhancing this functonal food value is an opportunity to improve consumer health, but strategies to do so cannot compromise yield or organoleptc propertes, which are highest priorites for farmers and consumer, respectvely. One promising strategy is the supplementaton of fertliser regimens with biostmulants, which are non-nutritve substances associated with species-specific improvements to crop growth, yield, and quality. Accordingly, the impacts of a biostmulant complex (BC) containing molasses, Aloe vera extract, and fish-hydrolysate is characterised herein for its potental to impact strawberry growth, yield, quality, and functonal food value. Results indicated that BC treatment significantly increased (p < 0.05) plant biomass and canopy area (growth), total fruit count and weight per plant (yield), fruit aroma and colour (quality), and antoxidant potental (functonal food value). The results presented highlight the potental utlity of biostmulants to the strawberry sphere, providing a strategy to enhance the fruit to the benefit of both farmers and consumers.

Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data.