Food Crops Research Articles

Physiological and Molecular Mechanisms of Rice Tolerance to Salt and Drought Stress: Advances and Future Directions.

Rice, a globally important food crop, faces significant challenges due to salt and drought stress. These abiotic stresses severely impact rice growth and yield, manifesting as reduced plant height, decreased tillering, reduced biomass, and poor leaf development. Recent advances in molecular biology and genomics have uncovered key physiological and molecular mechanisms that rice employs to cope with these stresses, including osmotic regulation, ion balance, antioxidant responses, signal transduction, and gene expression regulation. Transcription factors such as DREB, NAC, and bZIP, as well as plant hormones like ABA and GA, have been identified as crucial regulators. Utilizing CRISPR/Cas9 technology for gene editing holds promise for significantly enhancing rice stress tolerance. Future research should integrate multi-omics approaches and smart agriculture technologies to develop rice varieties with enhanced stress resistance, ensuring food security and sustainable agriculture in the face of global environmental changes.

Advancing sustainable rice production in the Vietnamese Mekong Delta insights from ecological farming systems in An Giang Province

Rice serves as a crucial staple food crop for half of the world's population. In the Vietnamese Mekong Delta (VMD), rice production plays a vital role in national food security. However, the majority of the existing intensified rice cultivation schemes in the VMD, which are typically traditional, have rendered many farmers' livelihoods unsustainable due to issues such as land degradation, water pollution, health risks, and low profitability. Therefore, it is imperative to explore alternative sustainable farming systems. This study investigates the benefits of two ecological farming systems, specifically organic rice and rice mixed with lotus, as alternatives to conventional rice farming in the upper VMD floodplain province of An Giang. These two farming systems have demonstrated long-term socioecological and economic advantages. On the one hand, they allow the introduction of rice products to the market at more affordable prices. Additionally, they contribute to improved water quality, improved soil fertility, and increased biodiversity such as bird, fish, and plant species compared to traditional rice farming systems. Although we acknowledge that the availability of floodwater poses a significant constraint for alternative farming systems, the business opportunities and socioecological benefits associated with these systems outweigh the limitations. Our findings provide evidence that ecological farming practices that support rice cultivation represent promising alternatives to sustainable rice production, which can help mitigate vulnerabilities in intensified rice farming systems and can be scaled up for other floodplain provinces in the VMD and beyond.

Improving complex agronomic and domestication traits in the perennial grain crop intermediate wheatgrass with genetic mapping and genomic prediction.

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r=0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Transcriptome analysis of maize resistance to Fusarium verticillioides

ABSTRACT Maize (Zea mays) is a crucial crop for both food and economic purposes. Fusarium verticillioides is known to cause ear rot in corn, leading to a reduction in corn yield. Understanding the regulatory network of maize resistance to this ear rot is crucial for identifying key genes associated with disease resistance. The study revealed that inbred line J1259 exhibited resistance to Fusarium verticillioides by increasing the activities of POD and CAT enzymes. Transcriptome analysis at 48 and 96 hours post-infection with Fusarium verticillioides revealed a large number of DEGs involved in plant-pathogen interactions, MAPK signaling pathways-plants and plant hormone signaling were significant up-regulated. Additionally, transcription factors related to hormone signaling, particularly the ARF family involved in auxin regulation, were enriched. These results lay a foundation for further investigations into the mechanisms underlying the disease resistance response of J1259.

Real-Time Detection and Counting of Wheat Spikes Based on Improved YOLOv10

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model’s feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research.

Identification and Characterization of Multi-Mycotoxins Recovered from Dry Corn in Selected Markets in Enugu Metropolis, Enugu State, Nigeria

Corn is an important food crop that forms a major meal plan in Nigeria. Its high nutritious value also makes it perfect for fungi to thrive and produce its metabolites. Mycotoxin is a fungi metabolite that is toxic to both humans and animals. This study aimed at isolation of fungi from corn vended in selected markets in Enugu metropolis, identification and characterization of mycotoxin produced from the isolates. A total of 9 corn samples were collected from 3 different markets in Enugu metropolis comprising of Emene ,Ogbete and New markets respectively . The samples were cultured on potatoes dextrose agar for isolation, isolates were identified and characterized by cultural, biochemical and microscopic analysis of the organisms; selected isolates were further characterized by polymerase chain reaction (PCR). The isolates were subjected for mycotoxin production by culturing in potatoe dextrose broth for 14 days at 35°C after which the filtrates were assayed for the presence of mycotoxin using gas chromatography-mass spectroscopy (GC-MS) and individual aflatoxins were further characterized using high performance liquid chromatography (HPLC). From the study, Fusarium and Aspergillus flavus occurred at 66% respectively, Aspergillus niger at 55%, Mucor spp at 77%, Penicillium spp at 33%, and Aspergillus fumigates at 44%. PCR amplified products of the selected isolates showed positive amplification at 650bp for internal transcribed spacer gene. Mycotoxins identified from isolates were aflatotoxin, ochratoxin A (OTA), citrinin, patulin, citreoviridin, T-2 toxin, deoxynivalenol (DON), diacetoxyscirpenol, zearalenone (ZEN), nivalenol, Ht-2 toxin, cyclopiazonic acid with ochratoxin A, Diacetoxyscirpenol and Ht-2 toxin being above consumption limit at 2.64ug/kg,1.27ug/kg and 424.48ug/kg respectively. The isolates showed varied concentration of individual aflatoxins comprising of B1, B2, G1 and G2. These aflatoxins were produced by Aspergillus flavus strain ZMXL11, Aspergillus flavus strain AF-13, and Penicillium citrinin PEN-65 among, B1 was found to be above the maximum limit at 99% in all the isolates.Aflatoxin has been found as to be a group 1 carcinogen implicated in liver cancer. Constant consumption of mycotoxin contaminated corn could lead to varying health risk that includes liver cancer, kidney and gastrointestinal disturbances. This study showed that corn sold in selected markets in Enugu metropolis were contaminated with mycotoxigenic fungi that produced high levels of mycotoxin concentrations which is of public health concern.

Cocoa vs. food crops: smallholder’s seasonal food access and extension support for climate adaptation in Ghana

ABSTRACT The food security implications of the production of cocoa compared to food crops and the phenomenon of the lean season amongst smallholder farmers have been well-researched. Yet, the literature has yet to consider how climate variability affects these dynamics. This study addresses this gap by comparing the contributions of cocoa and food crops to food stability and access during extreme and erratic weather events. It then compares government extension support for households’ climate adaptation of these crops. Our data draw on the case of smallholder cocoa farmers in Ghana’s Central Region and comprise a survey of 250 households, 16 focus group discussions, and 81 semi-structured interviews with smallholders, market sellers, and government officials. We find that food crops are essential for households’ food access during the cocoa lean season and when cocoa yields are reduced by climate change-related extreme or erratic weather events. However, food crop yields are themselves negatively affected by climate variability impacts. Furthermore, many farmers lack climate adaptation strategies or support for food crops as government extension services focus instead on export-driven cocoa. We argue that to promote households’ food stability, more smallholder extension support for food crop production and climate adaptation is urgently needed.

Chromosome-scale Salvia hispanica L. (Chia) genome assembly reveals rampant Salvia interspecies introgression.

Salvia hispanica L. (Chia), a member of the Lamiaceae, is an economically important crop in Mesoamerica, with health benefits associated with its seed fatty acid composition. Chia varieties are distinguished based on seed color including mixed white and black (Chia pinta) and black (Chia negra). To facilitate research on Chia and expand on comparative analyses within the Lamiaceae, we generated a chromosome-scale assembly of a Chia pinta accession and performed comparative genome analyses with a previously published Chia negra genome assembly. The Chia pinta and Chia negra genome sequences were highly similar as shown by a limited number of single nucleotide polymorphisms and extensive shared orthologous gene membership. However, there is an enrichment of terpene synthases in the Chia pinta genome relative to the Chia negra genome. We sequenced and analyzed the genomes of 20 Chia accessions with differing seed color and geographic origin revealing population structure within S. hispanica and interspecific introgressions of Salvia species. As the genus Salvia is polyphyletic, its evolutionary history remains unclear. Using large-scale synteny analysis within the Lamiaceae and orthologous group membership, we resolved the phylogeny of Salvia species. This study and its collective resources further our understanding of genomic diversity in this food crop and the extent of interspecies hybridizations in Salvia.

Social Economic Factors Driving Cassava Adoption in Teso South, Busia County, Kenya

Cassava's significance as a food crop is underscored by its ability to thrive in poor soils, withstand drought, and tolerate marginal conditions where other crops might fail. Despite its recognized importance and the introduction of improved cultivars, cassava output in Busia County remains poor, even though it is Africa's most frequently planted staple crop. This study analysed the factors influencing adoption of improved cassava cultivars among 167 smallholder farmers in Busia County, Kenya. The objective was to determine the social and economic factors influencing the adoption of improved cassava cultivars among smallholder farmers in Teso South, Busia County. Data was collected from the sampled farmers using a multistage sampling procedure in six wards. A Binary Logit model was used to investigate the effect of the selected social and economic, factors on the adoption of improved cassava cultivars. The results revealed that the average farm size in Teso South is 2 acres (48.2%), while that set aside for cassava cultivation by households is 0.25 acres (66.4%). Gender, age, marital status and education level did not influence the farm size nor the size set aside for cassava cultivation (p>0.5). Majority 127(76.0%) of the respondents indicated that they had not adopted improved cassava cultivars on their farms (χ2 = 27.04, d.f.=1, p<0.0001). From the study, it was denoted that economic factors such as financial resources, land size and market accessibility positively and significantly influenced the adoption of improved cassava cultivars among smallholder farmers in Teso South, Busia County (β=-0.148, t=-6.886, p<0.001). Social factors such as age, gender, education and household did not influence the adoption of improved cassava cultivars among smallholder farmers in Teso South, Busia County (β=0.011, t=-0.92, p=0.926). The fitting of the model was significant (F 0.05 (3, 163) =29.303, p< 0.001). Agricultural extension programs should focus on providing targeted training and support on innovative techniques and technologies, such as the adoption of improved cassava cultivars, to enhance farmers' capacities and resilience, regardless of their educational backgrounds

Marketing Information System for Maize Commodities in Soppeng Regency to Increase Agricultural Productivity and National Food Security

Maize in Soppeng Regency is a strategic commodity and has bright marketing prospects. Therefore, efforts to increase maize production need to receive great attention, because with sufficient availability, maize is one of the main food needs in the world after rice. It is expected that the community's need for maize can be met and the selling price remains stable. This research aims to increase the productivity of corn commodity marketing in Soppeng Regency based on the application of online marketing information systems, so that the market world can be wider and make it easier for farmers to sell their corn commodity products and become corn crop production centers, in order to increase the productivity of food crops, especially in Soppeng Regency. This study uses preliminary data on corn production from villages and sub-districts sourced from the Office of Food Crops, Horticulture, Plantations, and Food Security of Soppeng Regency which consists of 49 villages, 21 sub-districts and 8 sub-districts. The system development method uses the SDLC (System Development Life Cycle) system development technique, the application of this method starts from the field condition analysis stage, the system design stage, the system creation stage, the system testing stage, the system usage stage in the field. In addition, to meet the sustainability of the program, technical guidance on the use of the system will be provided to each user at the sub-district level. The results of this research are in the form of using an online-based corn commodity marketing information system

Potential impact of climate change on dietary grain protein content and its bioavailability-a mini review.

The changing global climate brings a gradual yet constant and adverse shift in crop production. Grain crop plants, particularly cereals and legumes, respond varyingly to adverse climate, including reduction in grain yield and changes to their nutrient densities. An understanding of specific changes to crop systems under differing climatic conditions can help in planning diets to meet human nutrient sufficiency. Grain protein content is also affected by adverse environmental factors. Deficits in protein yield, linked to changes in grain or seed protein and antinutrient concentrations, have been reported in major food crops when exposed to elevated carbon dioxide, high temperature, drought, and humidity. These changes, in addition to affecting the quantity of indispensable or essential amino acids (IAA), also impact their bioavailability. Therefore, it is important to assess consequences of climate change on grain protein quality. An important tool to measure grain protein quality, is measuring its digestibility at the level of the ileum and its IAA concentration, linked to a metric called the Digestible IAA Score (DIAAS). A minimally invasive technique called the dual isotope tracer technique, which measures IAA digestibility after simultaneous administration of two different intrinsically labelled protein sources, one a test protein (2H/15N) and one a reference protein (13C) of predetermined digestibility, has been used in evaluation of grain protein IAA digestibility, and promises more in the evaluation of changes based on climate. This review discusses climate induced changes to grain protein quality through the prism of IAA digestibility, using the dual isotope tracer technique.

Progress on the Effect of Drought on Sorghum Growth and Its Response Mechanisms

Drought is one of the key factors limiting the increase in crop yields in China and one of the main abiotic stresses affecting the growth and development of sorghum. With currence of global extreme climate become more frequent, the risk of sorghum suffering from drought stress is increasing. Although sorghum (Sorghum bicolor (L.)) is an important food crop, its growth and yield will be significantly affected under abiotic stresses (such as drought, salinity, high temperature and low temperature). Therefore, it is of great significance to study the response mechanism of sorghum drought stress. This study summarized the effects of drought stress on sorghum growth, including the effects of seed germination, leaves and roots. This study summarized the effects of drought stress on sorghum growth, including the effects of seed germination, leaves and roots. The response mechanisms of sorghum to drought stress was discussed from stomatal regulation, osmotic regulation and antioxidant system, and the ways to improve drought resistance of sorghum were prospected, so as to lay a foundation for further study on the morphology, physiological characteristics and molecular mechanism of drought resistance of sorghum and provide theoretical reference for workers engaged in sorghum production and related research.

Performance of Wheat Genotypes under Irrigated Conditions in Far Western Terai of Nepal

Wheat, the most important food crop in terms of food and nutrition security in Nepal, production could not meet the demand of growing population due to limited use of high yielding variety and use of inappropriate variety in the context of increasing heat stress during ripening. For assessing the performance of different wheat genotypes, this study was carried out during November 2022 to April 2023 at Agronomy Farm of Far Western University, Kailali, Nepal. The study was designed in Randomized complete block design (RCBD) with three replications. Eleven genotypes with nine Nepal lines and two Bhairhawa lines were evaluated and compared with a popular variety, Vijay for their performance at Tikapur condition. The result revealed the highest grain yield from NL 1506, recording 3.36 t/ha, which was statistically at par with NL 1452, NL 1488 and BL 5116 at 5% level of significance. The popular variety Vijay yielded only 3.02 t/ha which was statistically at par with NL 1638, but was inferior to NL 1450. Statistically poor performance was observed in the genotypes NL 1445 and BL 4984.

Optimum Seeding and Nitrogen Fertilizer Rates for Maximizing Yield and Sustaining Rain-Fed Lowland Rice (<i>Oryza Sativa </i>L.) in Fogera Plain North-western Ethiopia

Rice (<i>Oryza sativa</i> L.) is increasingly becoming an important food crop in Ethiopia. However, the average rice productivity in Ethiopia is estimated at 2.8 t ha<sup>-1</sup> which is much lower than the world average, 4.6 t ha<sup>-1</sup>. Its productivity is challenged due to a lack of appropriate and location-specific agronomic practices like the application of optimum doses of nitrogen fertilizer and the use of optimum seeding rates. Thus, a field experiment was conducted in Fogera district in South Gondar Zone, Northwestern Ethiopia during the 2021 main cropping season to determine the optimum dose of N level and seeding rate. The treatments comprised factorial combinations of four levels of N (134, 184, 234 and 284 kg ha<sup>-1</sup>) and four different seeding rates (60, 80, 100, and 120 kg ha<sup>-1</sup>). The experiment was laid out in a randomized complete block design and replicated thrice. All data on phenology, vegetative growth yield and yield-related parameters were collected and measured following scientific standards of each parameter. Those data were subjected to analysis of variance using the general linear model (GLM) procedures of SAS 9.0 version system. Economic analysis was also carried out by following CIMMYT partial budget analysis procedures. The analysis result showed the main effects of both, different levels of nitrogen and different rate of seeding, and their interactions showed highly significant effect on the number of both total and effective tillers, grain filling, panicle length, phonological parameters, totals spikelet’s, thousands seed weight, grain yield, biological yield and straw yield. Application of 184 kg N ha<sup>-1</sup> level with a seeding rate 100 kg ha<sup>-1</sup> gave the maximum grain yield (6,641 kg ha<sup>-1</sup>), the highest number of effective tillers per m<sup>2</sup> (792) and filled grains per panicle (95.34). Furthermore, the highest net return of ETB 118,850 with acceptable marginal rate of return (2,313%) was also obtained from the application of 184 kg N ha-<sup>1</sup> level and a seeding rate 100 kg ha<sup>-1</sup>. From the current research experiment, it is possible to conclude that increasing seeding rate beyond 100 kg ha<sup>-1 </sup>and N above 184 kg N ha<sup>-1</sup> is not economical yield of rice <i>“Selam’’ </i>variety in the study area. Rather, use of 184 kg N ha<sup>-1</sup> and seeding rate of 100 kg ha<sup>-1</sup> is promising for Lowland Rice (<i>Selam</i>) variety production under the rain fed condition in Fogera Plains and similar agro-ecologies of rain-fed growing area of Ethiopia for better economic and agronomic advantage.

Heterologous Expression of Sunflower HaHPT and HaTMT Genes Enhances Rice-Grain Vitamin E Content.

Insufficient dietary vitamin intake can lead to severe health conditions in humans. Improving the vitamin E (VE) content of food crops such as rice through breeding is an economical and effective means to alleviate this problem. In this study, Homogentisate phytyltransferase (HPT) and γ-tocopherol methyltransferase (γ-TMT), two genes derived from sunflower (Helianthus annuus L., a high VE species), were introduced into an elite rice (Oryza sativa L.) cultivar "Ningjing 7" for biofortification. We verified the successful expression of the two genes in multiple transformation events. High-performance liquid chromatography revealed that transgenic plants expressing either HaHPT alone or HaHPT and HaTMT accumulate more VE compared with the wild type. We also revealed that the level of α-tocopherol, the form of VE with the highest biological activity, had increased to 2.33 times in transgenic HaTMT plants compared with the wild type. Transcriptome analysis revealed that the expression levels of some chlorophyll synthesis pathway genes related to VE precursor synthesis significantly increased during grain filling in transgenic rice grains. No difference in agronomic traits was observed between the transgenic plants and their wild type except for a slightly reduced plant height associated with the transgenic plants. These data demonstrate that the heterologous expression of HaHPT gene is effective in increasing the total VE content, while HaTMT plays an important role in the relative abundance of α-tocopherol in rice grains. This study demonstrates a promising strategy for breeding rice with elevated VE content via metabolic engineering.

Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems

A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m−1), N (201.15–993.19 kg ha−1), P (3.00–96.00 kg ha−1), K (124.88–1110.71 kg ha−1), Ca (7.00–46.00 meq 100 g soil−1), and Mg (2.30–21.50 meq 100 g soil−1) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management.

Forecasting the Right Crop Nutrients for Specific Crops Based on Collected Data Using an Artificial Neural Network (ANN)

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops.

Exploring Endophytic Fungi as Natural Antagonists against Fungal Pathogens of Food Crops.

The yield and quality of cultivated food crops are frequently compromised by the prevalent threat from fungal pathogens that can cause widespread damage in both the pre-harvest and post-harvest stages. This paper investigates the challenges posed by fungal pathogens to the sustainability and yield of essential food crops, leading to significant economic and food security repercussions. The paper critiques the long-standing reliance on synthetic fungicides, emphasizing the environmental and health concerns arising from their widespread and occasionally inappropriate use. In response, the paper explores the potential of biological control agents, specifically endophytic fungi in advancing sustainable agricultural practices. Through their diverse symbiotic relationships with host plants, these fungi exhibit strong antagonistic capabilities against phytopathogenic fungi by producing various bioactive compounds and promoting plant growth. The review elaborates on the direct and indirect mechanisms of endophytic antagonism, such as antibiosis, mycoparasitism, induction of host resistance, and competition for resources, which collectively contribute to inhibiting pathogenic fungal growth. This paper consolidates the crucial role of endophytic fungi, i.e., Acremonium, Alternaria, Arthrinium, Aspergillus, Botryosphaeria, Chaetomium, Cladosporium, Cevidencealdinia, Epicoccum, Fusarium, Gliocladium, Muscodor, Nigrospora, Paecilomyces, Penicillium, Phomopsis, Pichia, Pochonia, Pythium, Ramichloridium, Rosellinia, Talaromyces, Trichoderma, Verticillium, Wickerhamomyces, and Xylaria, in biological control, supported by the evidence drawn from more than 200 research publications. The paper pays particular attention to Muscodor, Penicillium, and Trichoderma as prominent antagonists. It also emphasizes the need for future genetic-level research to enhance the application of endophytes in biocontrol strategies aiming to highlight the importance of endophytic fungi in facilitating the transition towards more sustainable and environmentally friendly agricultural systems.

Migration of Microplastics in the Rice–Duckweed System under Different Irrigation Modes

Microplastic (MP) pollution in agriculture is garnering growing concern due to its potential detrimental impact on soil properties and crop growth, particularly affecting staple food crops such as rice. Irrigation plays a crucial role in the migration of MPs. However, limited research has focused on how different irrigation modes affect the migration of MPs in paddy fields. To simulate real-world conditions, in this experiment, two different irrigation modes were set: shallow–frequent irrigation (FWI, I0) and controlled irrigation (CI, I1). The experiment also included treatments with and without duckweed (D0 and D1, respectively), as well as treatments with and without MPs (M0 and M1). This resulted in a total of eight treatments: I0M0D0, I0M0D1, I1M0D0, I1M0D1, I0M1D0, I0M1D1, I1M1D0, and I1M1D1. Our findings indicated that compared to CI, FWI significantly increased the MP concentration in the leakage but reduced the numbers of MPs in the first soil layer and adhered by duckweed. Notably, dry–wet cycles under CI induced soil cracking, and the MP concentrations in cracked areas were significantly higher than those of crack-free soil. Moreover, compared with the MP-free treatment, MP treatments significantly influenced rice root growth, such as enhancing the average root diameter by 13.44%, root volume by 46.87%, root surface area by 30.81%, and biomass aboveground by 26.13%, respectively. The abundance of some microorganisms was also significantly influenced by the relative mobility (RM) of MPs. Furthermore, the root length was positively correlated with Planctomycetota. Meanwhile, Actinobacteriota was negatively correlated with the root surface area, root volume, and branch number, and Bacteroidota was negatively correlated with the number of root tips. However, further research is needed to elucidate how MPs influence microorganisms and, in turn, affect rice root growth.

Bibliometric Computation Mapping Analysis of Publication Machine and Deep Learning for Food Crops Mapping using VOSviewer

Machine learning and deep learning are currently widely used in various fields, including remote sensing for food security. However, there is no research that specifically examines the interests, developments, and trends of this research in the future. This study aims to examine the development of machine and deep learning research for mapping food crops through a bibliometric approach with computational mapping analysis using VOSviewer. Article data was obtained from the Google Scholar database using the publish or perish reference manager application. The title and abstract of the article were used to guide the search process by referring to the keyword “Machine and Deep Learning Mapping Food Crops”. 114 relevant articles were discovered. Google Scholar-indexed articles over the last ten years, from 2014 to 2023, were used as study material. The results show that machine research and deep learning for mapping food crops can be separated into three terms: machine learning, deep learning, and plant mapping. The term “Crop Mapping” has 57 links for a total of 199 links. The term "machine learning" has 41 links for a total of 79 links, and the term "deep learning" has 26 links for a total of 41 links. The results of the analysis of machine development and deep learning publications for mapping food crops in the last 10 years show a constant increase. The peak of the increase occurred in 2021 and 2022, namely 25 articles published per year, respectively. This means that this research topic is still relatively new in terms of interest and exploration, therefore there is still room further research. We examine numerous articles that have been published on machine and deep learning for crop mapping and their relation to the field studied with VOSviewer. This review can serve as a starting point for further research in different domains