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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

Sustainable crop production under changing climate is crucial to feed the increasing population of the world. Efforts are underway to discover novel strategies to ensure global food security. Allelopathy is one such phenomenon that can help in this regard. It is a direct or indirect and positive or negative effect of plant species on other plant species and microorganisms, through the release of secondary metabolites known as allelochemicals. Brassica species are well known for their allelopathic potential as most of them endogenously produce potent allelochemicals such as glucosinolates, allyl isothiocyanates, and brassinosteroids. These allelochemicals are highly phytotoxic to target species when released at high concentrations and, therefore, affect their growth and development. This review illustrates the potential role of Brassica allelopathy for crop production in modern agriculture. Allelopathic potential of Brassica species can be utilized for weed management by using them as cover crops, companion crops, and intercrops, for mulching and residue incorporation, or simply by including them in crop rotations. Similarly, the expression of allelochemicals from these species have great value in the management of crop pests and diseases, and abiotic stresses. Most of these allelochemicals can also act as crop growth promoters when released or applied at low concentrations. Although the use of chemical herbicides, pesticides, and synthetic growth regulators is currently inevitable for crop production, the use of ecological options like allelopathy may help in achieving global food security sustainably. Exploring the potential of Brassica allelopathy could be promising in achieving higher productivity without compromising the environmental safety.

Image segmentation is a crucial task in computer vision, which divides a digital image into multiple segments and objects. In agriculture, image segmentation is extensively used for crop and soil monitoring, predicting the best times to sow, fertilize, and harvest, estimating crop yield, and detecting plant diseases. However, image segmentation faces difficulties in agriculture, such as the challenges of disease staging recognition, labeling inconsistency, and changes in plant morphology with the environment. Consequently, we have conducted a comprehensive review of image segmentation techniques based on deep learning, exploring the development and prospects of image segmentation in agriculture. Deep learning-based image segmentation solutions widely used in agriculture are categorized into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks, graph neural networks, instance segmentation networks, and transformer-based models. In addition, the applications of image segmentation methods in agriculture are presented, such as plant disease detection, weed identification, crop growth monitoring, crop yield estimation, and counting. Furthermore, a collection of publicly available plant image segmentation datasets has been reviewed, and the evaluation and comparison of performance for image segmentation algorithms have been conducted on benchmark datasets. Finally, there is a discussion of the challenges and future prospects of image segmentation in agriculture.

Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato ( Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

This study investigated the shifts in irrigation water demand and supply of the major staple and water-intensive crops (wheat and rice) in the Indus, Ganges and Brahmaputra (IGB) river basins of South Asia under the combined impacts of climate change and socio-economic development during the period 1981–2100. It explores irrigation water usage during climate-sensitive crop growth phases (i.e. vegetative and reproductive which required ~ 60% of the total seasonal (sowing to harvest) water demand), which is supposed to be crucial for long-term integrated crop water management. A hydrology vegetation model Lund Potsdam Jena Managed Land is forced with an ensemble of eight downscaled (5 arc-min) global climate model’s using the RCP (Representative concentration pathways) -SSP (Shared socio-economic pathways) framework, i.e. RCP4.5-SSP1 and RCP8.5-SSP3. To investigate phase-specific crop water projections, trend analysis is performed. It shows a significant (p<0.001) increase in irrigation water demand during the vegetative phase of wheat (6 mm) and reproductive phase of rice (26 mm) and a decrease during the reproductive phase of wheat (13 mm) and vegetative phase of rice (11 mm) in selected study sites. The large decrease in projected irrigation demand for wheat can be explained by a shortening of the growing season length as a result of rising temperatures and increased precipitation. Whereas, an increase in irrigation demand for rice is a combined effect of higher temperatures and less precipitation during the reproductive phase in the region. At the same time, irrigation supply by surface water and groundwater is likely to change in future due to warmer and drier growing periods, causing a significant increase in groundwater irrigation, mainly for rice. Our major research findings show the importance of crop water assessments during the sensitive crop growth phases of wheat and rice which vary in space and time. Including crop phase-specific, climate impact assessments of regional and global projection will help improve the region’s existing crop-water management strategies and adaptation practices.

In some of our most useful crops (such as rice and wheat), photosynthesis produces toxic by-products that reduce its efficiency. Photorespiration deals with these by-products, converting them into metabolically useful components, but at the cost of energy lost. South et al. constructed a metabolic pathway in transgenic tobacco plants that more efficiently recaptures the unproductive by-products of photosynthesis with less energy lost (see the Perspective by Eisenhut and Weber). In field trials, these transgenic tobacco plants were ∼40% more productive than wild-type tobacco plants. Science , this issue p. eaat9077 ; see also p. 32 Tobacco plants carrying engineered glycolate metabolic pathways showed as much as 40% greater productivity than wild-type plants in field trials. Photorespiration is required in C 3 plants to metabolize toxic glycolate formed when ribulose-1,5-bisphosphate carboxylase-oxygenase oxygenates rather than carboxylates ribulose-1,5-bisphosphate. Depending on growing temperatures, photorespiration can reduce yields by 20 to 50% in C 3 crops. Inspired by earlier work, we installed into tobacco chloroplasts synthetic glycolate metabolic pathways that are thought to be more efficient than the native pathway. Flux through the synthetic pathways was maximized by inhibiting glycolate export from the chloroplast. The synthetic pathways tested improved photosynthetic quantum yield by 20%. Numerous homozygous transgenic lines increased biomass productivity between 19 and 37% in replicated field trials. These results show that engineering alternative glycolate metabolic pathways into crop chloroplasts while inhibiting glycolate export into the native pathway can drive increases in C 3 crop yield under agricultural field conditions.

Aim Cereal-legume intercropping can result in yield gains compared to monocrops. We aim to identify the combination of crop traits and management practices that confer a yield advantage in strip intercropping. Methods We developed a novel, parameter-sparse process-based crop growth model (Minimalist Mixture Model, M 3 ) that can simulate strip intercrops under well-watered but nitrogen limited growth conditions. It was calibrated and validated for spring wheat ( Triticum aestivum ) and spring faba bean ( Vicia faba ) grown as monocrops and intercrops, and used to identify the most suitable trait combinations in these intercrops via sensitivity analyses. Results The land equivalent ratio of intercrops was greater than one over a wide range of nitrogen fertilizer levels, but transgressive overyielding, with total yield in the intercrop greater than that of either sole crop, was only obtained at intermediate nitrogen applications. We ranked the local sensitivities of the individual yields of wheat and faba bean of the whole intercrop under various nitrogen input levels to various crop traits. Conclusions The total intercrop yield can be improved by selecting specific traits related to phenology of both species, as well as light use efficiency of faba bean and, under high nitrogen applications, of wheat. Changes in height-related crop traits affected individual yields of species in intercrops but not the total intercrop yield.

Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM). SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES). Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha−1 (normalized root mean square error, NRMSE of 5% and 8%) for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with peculiarity in the timing of rice establishment, leaf growth, and yield level, and thus contributing to the spatial targeting of further investigation and interventions needed to reduce yield gaps.

AimBalanced crop nutrition is key to improve nutrient use efficiency and reduce environmental impact of farming systems. We developed and tested a dynamic model to predict the uptake of P and K in long-term experiments to better understand how changes in soil nutrient pools affect nutrient availability in crop rotations.MethodsOur RC-KP model includes labile and stable pools for P and K, with separate labile pools for placed P and organic fertilizers including farm yard manure (FYM). Pool sizes and crop-specific relative uptake rates determined potential uptake. Actual crop uptake from labile pools was based on concepts developed by Janssen et al. (Geoderma 46:299-318, 1990). The model was calibrated on three long-term experiments from Kenia (Siaya), Germany (Hanninghof) and the United Kingdom (Broadbalk) to estimate parameter values for crop-specific relative uptake rates and site-specific relative transfer rates.ResultsThe model described N, P and K uptake accurately with a Nash-Sutcliff modelling efficiency of 0.6–0.9 and root mean squared errors of 2.6–3.4 kg P ha−1 and 14–20 kg K ha−1. Excluding organic labile pools did not affect model accuracy in Broadbalk in contrast to Hanninghof where Mg deficiencies affected crop uptakes in treatments without Mg or FYM.ConclusionsThis relatively simple model provides a novel approach to accurately estimate N, P and K uptake and explore short- and long-term effects of fertilizers in crop rotations. Interactions between limiting nutrients affecting actual nutrient uptake were captured well, providing new options to include N, P and K limitations in crop growth models.

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R2 of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions.