Crop Conditions Research Articles

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Microbial Decomposer for Crop Residue Management in Rice-Wheat Cropping System.

Crop residue management is vital in the Rice-Wheat cropping system, influencing soil health and crop productivity. This study examined the effects of organic and inorganic fertilizers and microbial decomposers on rice growth and yield. We evaluated seven treatments: 100% recommended dose fertilizer (RDF); 50% residue + 50% RDF; 50% residue + 50% RDF + Pusa decomposer; 50% residue + 50% Green Manuring (GM)/Green Leaf Manuring (GLM); 50% residue + 50% GM/GLM + Pusa decomposer; Residue @ 2.5 tons per acre + Pusa decomposer; Residue @ 2.5 tons per acre + no Pusa decomposer; and absolute control. Results indicate that integrating organic and inorganic fertilizers with microbial decomposers positively affects rice growth and yield parameters. While adding microbial decomposer to RDF did not consistently enhance rice yield, it improved soil enzymatic properties. This suggests that the effectiveness of microbial decomposers may vary based on specific soil and crop conditions. Therefore, microbial decomposers present a promising approach to boost soil health and fertility. Further research is needed to optimize conditions for their use and systematically assess their impact on crop yields.

Enhancing Irrigation Management: Unsupervised Machine Learning coupled with Geophysical and Multispectral Data for Informed Decision-Making in Rice Production

Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI-PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p<0.01) with multispectral data (-0.25 to -0.45) for fields 1a, 1b and 4a. On the other hand, there was a direct correlation (p<0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a. For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions.

Vegetation community recovery on restored bottomland hardwood forests in northeast Indiana, USA.

Vegetation communities in restored bottomland hardwood forests in northeast Indiana were studied 6-21 years after restoration to assess progress toward restoration objectives. The study focused on four sites that were restored to compensate for resource injuries after contaminant releases. The restored sites were compared with four reference-site conditions, including crops (prerestoration condition), old field communities representing a no-management alternative, locally sampled second-growth mature forests, and forest community types described by the US National Vegetation Classification (USNVC), which represent ideal or defining conditions of recognized vegetation communities. Fixed-area plots provided data on field-sampled environmental variables, vegetation, soil, and hydrological conditions for crops, old fields, restored areas, and mature forests. The USNVC database provided quantitative data for three historically and geographically relevant reference forest community types for comparison with the sampled communities. Results of nonmetric multidimensional scaling based on species cover revealed clear gradients relating to site age and canopy development. Along those gradients, restored areas demonstrated increasing similarity to mature forest reference communities in terms of floristic composition. Specifically, the floristic quality of restored areas was significantly greater than that of crops and old fields. Furthermore, soil health measurements of physical, chemical, and hydrological conditions indicated significant improvements in restored site soils compared with prerestoration conditions represented by cropland soils. Descriptions and data from the USNVC provided ecological context for restoration target conditions and facilitated the assessment of restoration recovery along a trajectory from starting conditions to those target conditions. Descriptions by USNVC also helped identify deviations from the intended restoration objectives (e.g., invasive species recruitment) and potential adaptive management actions to return sites to their intended trajectories. Integr Environ Assess Manag 2024;20:1917-1938. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Nitrogen fertilization and sowing date as wheat climate change adaptation tools under Mediterranean conditions

In the current situation, climate change has substantially disturbed precipitation occurrence in the Mediterranean region, by increasing its variability and decreasing the total annual amount, which both negatively affect rainfed crop productivity. We hypothesize that a simple cost-effective method for enhancing crop adaptation to new climate conditions would consist of modifying the crop sowing date. Traditional nitrogen (N) fertilization rates could also be adjusted to the current situation given the interdependent water/N relation in plant nutrition. Based on this hypothesis, during a 4-year field experiment with bread wheat (Triticum aestivum L., var. Pistolo), the effects of three sowing dates (October, November, February) and three N fertilization rates (54kgN ha-1, 27kgN ha-1, 0kgN ha-1) on crop development, yield, grain quality, soil N content and N use efficiency were analyzed. The results showed that water scarcity was the predominant limiting factor, because it outweighed N deficiency with half-fertilized crops being as productive as fully fertilized treatments. Nevertheless, sowing date was the most influential factor, with up to a 30% yield increase noted for the November-sown wheat compared to that sown in October, while delaying wheat sowing to February decreased crop yields. Grain protein content remained the same between the November- and October-sown crops, but increased in the February one crops. Optical sensor measurements showed that an optimal assessment of the current water/N nutritional status of crops can be achieved with these tools.

The influence of meteorological conditions of the Non-Chernozem zone on the phytosanitary condition of grain bean crops

There is a tendency to expand the area of bean cultivation in the northern direction, which is due to climatic changes and breeding achievements. There are 191 varieties of beans registered in the state register of breeding achievements approved for use, of which 31 varieties are recommended for cultivation in the belt between 50° and 60° latitude. The relevance of bean cultivation in the Non-Chernozem zone is increasing. This is due to the increased demand for beans, which are characterized by high nutritional value. Also, this crop is promising as an alternative in grain crop rotations. The study of the phytosanitary condition of bean plants in potentially new cultivation regions in order to develop a crop protection system is of interest. The promotion of crops to new territories is inevitably accompanied by phytosanitary risks, which have been little studied due to insufficient information about bean diseases. The literature provides an assessment of potential threats. However, their manifestation is closely related to the climatic and weather conditions of the growing season. The analysis of the interrelationships of the phytosanitary condition of crops with the temperature and humidity regimes of the bean growing period is relevant.

Studies of genetic diversity and genome-wide association for vitamin C content in lettuce (Lactuca sativa L.) using high-throughput SNP arrays.

Lettuce (Lactuca sativa L.) is a source of beneficial compounds though they are generally present in low quantities. We used 40K Axiom and 9K Infinium SNP (single nucleotide polymorphism) arrays to (i) explore the genetic variability in 21 varieties and (ii) carry out genome-wide association studies (GWAS) of vitamin C content in21 varieties and a population of 205 plants from the richest variety in vitamin C ('Lechuga del Pirineo'). Structure and phylogenetic analyses showed that the group formed mainly by traditional varieties was the most diverse, whereas the red commercial varieties clustered together and very distinguishably apart from the rest. GWAS consistently detected, in a region of chromosome 2, several SNPs related to dehydroascorbic acid (a form of vitamin C) content using three different methods to assess population structure, subpopulation membership coefficients, multidimensional scaling, and principal component analysis. The latter detected the highest number of SNPs (17) and the most significantly associated, 12 of them showing a high linkage disequilibrium with the lead SNP. Among the 84 genes in the region, some have been reported to be related to vitamin C content or antioxidant status in other crops either directly, like those encoding long non-coding RNA, several F-box proteins, and a pectinesterase/pectinesterase inhibitor, or indirectly, like extensin-1-like protein and endoglucanase 2 genes. The involvement of other genes identified within the region in vitamin C levels needs to be further studied. Understanding the genetic control of such an important quality trait in lettuce becomes very relevant from a breeding perspective.

Identification of drought-salinity combined stress in tomato plants by vegetation indices

A major issue in several farming areas of the Mediterranean basin consists of drought and salinity stress. This stress is mainly due to a steady exposition of warm daily temperature and heatwaves, moreover with inevitable irrigation with saline water. Therefore, detecting the stress is essential to minimise significant yield loss and preserve agricultural sustainability. In this context, remote and proximal sensing can play a crucial role in allowing fast, not destructive, extensive, and reliable assessment of crop status. In this work, the effectiveness of several multispectral indices in detecting salinity and water stress in tomato plants, grown under controlled green-house conditions, was investigated. Three different classifiers (fine tree model, linear discriminant model, and linear support vector machines model) were used to verify whether, and the extent to which, the adopted multispectral indices can be adopted to identify a stress condition of the tomato plants. In the experimental campaign, the stress occurrence on tomato plants was assessed on the base of a set of ecophysiological measurements, such as transpiration, stomatal conductance, and photosynthesis rate. Obtained results showed that a classification model based on linear support vector machines, exploiting the combination of Photochemical Reflectance Index and the Chlorophyl Index, can detect drought and salinity stress in tomato plants with an accuracy higher than 94%.

Hidden Archaeological Remains in Heterogeneous Vegetation: A Crop Marks Study in Fortified Settlements of Northwestern Iberian Peninsula

This study evaluates the effectiveness of multispectral imaging via Unmanned Aerial Systems (UAS), in combination with advanced digital image processing techniques, for the detection and mapping of archaeological sites within diverse landscapes. The research focuses on six case studies located in the northwest of the Iberian Peninsula, a region marked by complex vegetation patterns and varying topography. The primary objective is to assess the potential of these non-invasive remote sensing techniques in identifying crop marks associated with buried structures from ancient, fortified settlements. By means of Principal Component Analysis (PCA) and vegetation indices, the study aims to pinpoint areas of interest that may indicate the presence of archaeological features, while effectively distinguishing them from modern disturbances or natural terrain variations. The research encountered several challenges, including seasonal variations in crop conditions and recent land-use changes. The methodology successfully identified distinct archaeological features. In some instances, natural vegetation variability, typically seen as an obstacle, enhanced the visibility of crop marks, aiding in the detection of underlying structures. These results offer a cost-effective and scalable option for preliminary archaeological surveys, particularly in refining survey methodologies and guiding future excavation efforts aimed at uncovering and preserving ancient, fortified settlements in the region.

Impacts of adaptation to climate change on farmers’ food security and level of sesame production in Western Ethiopia

The existence of multiple stresses and poor adaptive capacity make Africa most susceptible to climate change. In Ethiopia the potential adverse effects of climate change on the agricultural sector, the main stay of the country’s economy, are major concerns. To ensure food security, reducing the vulnerability of agricultural systems through different feasible adaptation strategies is one of the policy options in response to climate change impact This study is a first of its kind in examining the relative effectiveness of various adaptation strategies in ensuring farmers’ food security and enhancing level of sesame production in rural area of Western Ethiopia. In addition to data obtained from meteorological stations, cross-sectional data were collected interviewing 400 farm households. Descriptive statistics, two-stage least square (2SLS) and double-hurdle (D-H) models were used to analyze the data. The results of the study indicate that households are adapting using various strategies to the looming climate change in the area. The study also indicated that, though sesame production was negatively impacted by the climate hazards, smallholders have continued its production at minimum level due mainly to its high value crop character. 2SLS estimation results revealed that rainfall and temperature variability have negative impact on household’s food security. Moreover, the result indicates effectiveness of climate adaptation strategies namely agronomic practices, irrigation and soil and water conservation in reducing climatic risks and ensuring household food security. The result also implicitly indicated that farmers continued to adapt sesame production under risk climate and it is contributing to farmers’ food security. Further, the result revealed that climate change adaptation strategies have positively impacted the level of sesame production. Consequently, policy that augments households’ climate awareness and promotes adaptation decision and strategies could help reducing risks pertaining to climate and thereby improves farmers’ food security status and production of high value export potential crop—sesame.

Environmental impacts and nitrogen-carbon-energy nexus of vegetable production in subtropical plateau lake basins.

Vegetables are important economic crops globally, and their production has approximately doubled over the past 20 years. Globally, vegetables account for 13% of the harvested area but consume 25% of the fertilizer, leading to serious environmental impacts. However, the quantitative evaluation of vegetable production systems in subtropical plateau lake basins and the establishment of optimal management practices to further reduce environmental risks are still lacking. Using the life cycle assessment method, this study quantified the global warming, eutrophication, acidification, and energy depletion potential of vegetable production in a subtropical plateau lake basin in China based on data from 183 farmer surveys. Our results indicated that vegetable production in the study area, the Erhai Lake Basin, was high but came at a high environmental cost, mainly due to low fertilizer efficiency and high nutrient loss. Root vegetables have relatively high environmental costs due to the significant environmental impacts of fertilizer production, transportation, and application. A comprehensive analysis showed that the vegetable production in this region exhibited low economic and net ecosystem economic benefits, with ranges of 7.88-8.91 × 103 and 7.35-8.69 × 103 $ ha-1, respectively. Scenario analysis showed that adopting strategies that comprehensively consider soil, crop, and nutrient conditions for vegetable production can reduce environmental costs (with reductions in global warming potential (GWP), eutrophication potential (EP), acidification potential (AP), and energy depletion potential (EDP) by 10.6-28.2%, 65.1-73.5%, 64.5-71.9%, 47.8-70.4%, respectively) compared with the current practices of farmers. This study highlighted the importance of optimizing nutrient management in vegetable production based on farmers' practices, which can achieve more yield with less environmental impacts and thereby avoid the "trade-off" effect between productivity and environmental sustainability.

Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions

The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with R2, RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.

Monitoring Crop Condition at Gield Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR)

Operations that rely on optically-based Normalized Difference Vegetation Index (NDVI) to track crop productivity, are negatively impacted by cloud cover. In this research, parameters from Synthetic Aperture Radar (SAR) imagery were calibrated to NDVI with the targeted outcome to integrate SAR into Canadian crop monitoring services. Data from fully polarimetric RADARSAT-2 and dual-polarization Sentinel-1B imagery were calibrated to Sentinel-2 NDVI. Random Forest Regression (RFR) and Least Squares Boosting (LSBoost) were tested to calibrate SAR to NDVI (SARcal-NDVI) for six crops (corn, canola, soybeans, wheat, oats and barley). RADARSAT-2 provided the best fit with R2 exceeding 0.84 for canola, 0.87 for wheat and 0.90 for corn and soybeans. Correlations were lower for oats (R2 of 0.72–0.77) and barley (R2 of 0.43–0.64) due to a limited number of fields. Although a global model yielded lower correlations (R2 of 0.80), this model would be easier to implement into operations. Total power, the first and second eigenvalues and VH backscatter were important in reducing model error. The SARcal-NDVI and Growing Degree Days were then integrated into a Crop Structure Dynamic Model to produce daily estimates of crop condition, at field scales. The next step is to test if these daily estimates of crop condition can improve crop yield forecasting for the Canadian agriculture sector.

INNOVATIVE TECHNOLOGIES IN SPRINKLER IRRIGATION: AN OVERVIEW

The overuse of water in conventional irrigation at the farm level is a global issue that can be addressed by improving irrigation techniques. Sprinkler irrigation can reduce water losses by carefully planning the crop, soil, landscape, and weather conditions, as well as installing the proper sprinkler hardware. The application efficiency of sprinkler systems may be enhanced when these systems are coupled with automation for precise irrigation. Sprinkler irrigation has advanced significantly, but new ideas and current practices are driving further progress. These novel ideas include low-pressure sprinklers, smart controllers, fertigation, water-pesticide integrated sprinkler systems, and variable-rate irrigation. The objective of this work is to highlight the introduction of innovative technologies to optimize sprinkler irrigation systems. Suggestions for the future development of sprinkler irrigation technology are offered to ensure its long-term viability.

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

Crop Classification System in Agriculture

Agriculture plays a crucial role in Malaysia's economy, providing employment opportunities, income, and ensuring food security. The country's diverse climate and geography contribute to a wide range of agricultural products. However, farmers face challenges in identifying crop diseases and damage, leading to time‐consuming inspections and potential human errors during large‐scale crop harvesting. In order to address these issues, this project aims to design and develop a computer vision‐based system for classifying the condition of crops. By leveraging computer vision, the system eliminates the need for manual inspection and empowers farmers with an efficient and accurate tool for assessing crop quality. Visual cues, such as colour are automatically analysed by the YOLOv8 model to detect signs of damage in each chili crop. The crop classification system was evaluated using a separate dataset consisting of 300 chili samples. The results demonstrated the effectiveness of the YOLOv8 model, achieving an impressive accuracy of 99.67%. The system exhibited perfect precision in identifying bad chilis and a recall rate of 99.33% for capturing the majority of bad chilis present in the dataset. This study signifies the potential of the YOLOv8 model as a reliable tool for predicting chili crop quality. By implementing this system, farmers can save significant time and effort previously spent on manual inspections.

Spatial Chlorophyll Estimation from Rice Field using Drone-derived Spectral Indices

Background: Precision farming has significantly advanced the agricultural sector by enabling real-time canopy monitoring and crop condition evaluation, thus facilitating precise management strategies to enhance yields. This study investigated the use of drone-derived vegetation indices (VIs) for assessing spatial variability in crop conditions, offering a more cost effective and practical alternative to satellite data. Methods: The field experiment was conducted during the Kuruvai season (July - November 2023) on a short-duration rice variety CO 55. Several vegetation indices viz., BGI, CI, EVI, GNDVI, MCARI, MSAVI, NDRE and NDVI were calculated to predict chlorophyll content and correlated with the measured SPAD values. Result: The results showed that indices like MCARI, GNDVI, and NDVI had strong positive correlations with SPAD values, with MCARI exhibiting the highest correlation (R = 0.914) and an R² value of 0.836. The findings underscore the effectiveness of using drone-derived indices for precise chlorophyll estimation, which is crucial for variable rate fertilizer application in precision agriculture.

Estimating Energy Needs for Climate-Controlled Greenhouses in Syria with a Software Tool

Amid the current conditions in Syria, the study of energy consumption within plastic greenhouses emerges as a fundamental element in the agricultural economy, especially in areas subject to extreme climate variations. With many thermal power stations ceasing operation due to conflicts and the diminishing sources of energy, understanding energy consumption becomes more urgent to enhance productivity and reduce costs. Successful management of protected agriculture requires in-depth knowledge of weather dynamics and the optimal environmental conditions for crops. To implement effective management of plastic greenhouses, it is essential to recognize how climatic fluctuations affect plant growth and production throughout the various seasons. Heating systems form a significant part of the costs in constructing plastic greenhouses, and deficiencies in these systems can lead to negative impacts on quality, quantity, duration of cultivation, and production volume. Therefore, accurately calculating heating costs is crucial for reducing operational expenses. This study included the development of a computer program to determine the heating needs of plastic greenhouses, considering various factors such as the geographical location of the greenhouse, crop type, covering materials, heating system used, and land area. The results showed that Syria needs 4.56 megawatts of energy for the greenhouses, with the Tartus Governorate consuming the largest share, with energy consumption rates in Tartus, Latakia, Homs, and Damascus countryside amounting to 3.6, 0.3, 0.51, and 0.19 megawatts, respectively. The crops of tomatoes, vegetables, strawberries, and tropical plants consumed 2.2, 1.66, 2.21, and 0.244 megawatts of energy, respectively. This study is an important step towards achieving sustainable and efficient agriculture that contributes to supporting the economy and protecting the environment in Syria.

Progress in Research on Deep Learning-Based Crop Yield Prediction

In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research.

Digital Metrics of Agro-Industrial Complex: Intelligent Monitoring of Grain Production

Abstract The article examines the basic conditions for the digitalization of grain production monitoring, including data collection organization, IT infrastructure selection, as well as changes in organizational culture and personnel training. The digitalization of the monitoring process involves the use of modern technologies such as neural networks and computer vision to enhance the accuracy and promptness of crop condition monitoring. Special attention is given to the implementation of digital solutions and overcoming barriers that hinder the digital transformation of grain fields. The principles of monitoring organization presented in the article lay the foundation for efficient and sustainable production, ensuring the quality and safety of grain products, contributing to the strengthening of the country’s food security, enhancing the resilience of the agro-industrial complex, and can become a conceptual basis for the development of the intelligent component of grain production.