Crop Conditions Research Articles

The Current Status of Wheat Crop in Sinai Region

The Current Status of Wheat Crop in Sinai Region

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant’s nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Enhancing Plant Disease Recognition on Mobile Devices: A Hybrid Approach with CNNs, Rule-Based Systems, and Machine Learning

The food industry has significantly boosted the agricultural economy of India, historically known as the largest producing nation with a strong agricultural identity. The primary crops include grains, fruits (such as potatoes, oranges, tomatoes), sugarcane, and specially cultivated grains and cotton. Maharashtra, a state in India, has experienced substantial economic growth, particularly attributed to citrus and cotton industries. This growth has generated employment opportunities and holds significant potential for the state's economic advancement. To sustain the prosperity of these industries, the government is actively addressing concerns related to disease control, Labor costs, and global market dynamics. In recent times, citrus canker, citrus greening, and black spots on cotton have emerged as severe threats to citrus crops in Maharashtra. Farmers are troubled by the costs associated with tree loss, scouting efforts, and the use of chemicals to control diseases. An automated detection system could play a crucial role in preventing and minimizing these losses, thereby safeguarding the industries, farmers, and the overall economy. This research focuses on developing disease detection through pattern recognition methods for various crops. The image detection process has some main categories which are image acquisition, image processing, and pattern recognition. Image preprocessing is used to process the image in a clear and clean format. Pattern recognition methods are then utilized to classify samples into different crop conditions. To assess classification approaches, results will be compared across different crops for disease detection. The goal is to demonstrate a classification accuracy surpassing existing models, reaching the highest. The research primarily aims to showcase the feasibility of disease detection based on visible symptoms on fruits or leaves. Data collection and initial knowledge acquisition are planned through offline and online approaches. The overall motive is to have accurate detection of the fruits and leaves thereafter using those results for diseases detection. Keywords: Machine Learning Algorithms, Plant disease detection, Image Recognition, Rule-Based mechanism, CNN, Detection technologies, Color Grading

Influence of Nitrogen Application Rate on Wheat Grain Protein Content and Composition in China: A Meta-Analysis

The nitrogen application rate (NAR) has a significant effect on the contents of wheat grain protein and its composition. There is still no consensus regarding the appropriate NAR, given the differences in studied conditions and influence of factors such as geographical location, climate, and soil nutrient contents. In this study, 66 papers related to wheat grain protein and its composition published from 1984 to 2021 were selected for meta-analysis in comprehensively evaluating the response of wheat grain protein content and composition to NAR in China. The results reveal that NAR significantly increased total protein content by 9.49–28.6%, gliadin by 9.13–30.5%, glutenin by 12.9–45.4%, albumin by 5.06–15.8%, and globulin by 8.52–24.0% of wheat grain in China, respectively, compared to no nitrogen application. The optimal NAR is 240–300 kg ha−1 when specific planting conditions are not being considered. Under different growing conditions, the NAR that provided the greatest increase in wheat grain protein and its composition varied as follows: 180–240 kg ha−1 in Northwest China and at >100 m altitudes; >300 kg ha−1 in North China and at <100 m altitudes and lower soil base nutrient levels; 240–300 kg ha−1 in Southeast China, with higher soil nutrients levels and for all average annual temperatures and precipitation ranges. In conclusion, the results of the present study reveal that it is feasible to systematically enhance the contents of wheat grain protein and its related fractions by appropriate NAR under different cropping conditions.

Design, Fabrication and Monitoring of Automated Polyhouse

The agriculture sector faces the challenge of feeding a growing population while dealing with the effects of climate change. With the limited expansion of cropland, agriculture automation has become necessary. Internet of Things (IoT) and Artificial Intelligence (AI) have made significant strides in various industries, including agriculture. These digital technologies offer smart systems that can monitor, control, and visualize farm operations in realtime, approaching human expertise. The potential applications of IoT and AI in agriculture include smart machinery, irrigation systems, pest control, crop monitoring, and more. Automated polyhouses equipped with IoT and AI components can optimize crop conditions, enhancing yield and quality while minimizing climate impacts. Over the past decade, research in this field has matured, transitioning from conceptual stages to implementation. The integration of AI and IoT into agriculture practices poses technical challenges, but understanding these technologies will facilitate their adoption and the development of IoT and AI-based solutions in farming.

Phytomonitoring of medicinal plants: pests and diseases in the VILAR botanical garden

Medicinal plants represent a separate group of plants that requires an environmentally safety approach to their protection from harmful organisms. The biotic factor significantly affects on the productivity of medicinal plants and the quality of medicinal raw materials obtained from them.The VILAR Botanical Garden is a unique scientific and crop production site, its main task is the conservation of rare species of medicinal plants, as well as the development agrotechnologies for species recently taken from nature, or another regions and countries. However, when we growing plants in biocollections on one place for a number of years, there is quite favorable conditions for the reproduction of pests and the spread of diseases. The monitoring results make it possible to monitor and predict the condition of crops on experimental plots and in biocollections. At the same time, a purposeful systematic study of plant pests and diseases in the VILAR Botanical Garden has not been carried out in recent years. Our studies was conducted in 2022–2023 years on 63 species of medicinal plants and allowed to clarify the species composition of pests, including 26 species of phytophages and 9 species of pathogenic fungi. The phytosanitary condition of the regions of the Botanical Garden and the crops on the experimental field was satisfactory, there was no loss of species or crops from the collection due to severe disease or pest damage. Representatives of the family of leaf beetles (Chrysomelidae) and true aphids (Aphididae) had the greatest prevalence among pests, among the diseases — powdery and rusty.

IoT Based Crop Monitoring System

The IoT-Based crop monitoring System is a cutting-edge solution designed to enhance agricultural productivity and efficiency by utilizing sensor technology and automation. The core objective is to automate the irrigation process based on real-time soil moisture data. The systememploysanESP8266 module to facilitate the seamless exchange of data between various sensors and Firebase, a real-time cloud database. This interconnected system comprises two primary fields, each equipped with soil and soil moisture sensors, temperature sensors, smoke detectors, UV light, buzzer, solenoid valves, and mini water pump. The ESP8266 module serves as the central hub for collecting data from the sensors and transmitting it to the Firebase database. The data includes crucial information such as soil moisture levels, temperature, smoke detection, and UV light intensity. Furthermore, the system integrates alarm functionality through the buzzer and precise irrigation control via the solenoid valve and mini water pump. The data stored in Firebase is accessible through a web application built using React.js and Node.js. This web interface provides users with a graphical representation of the gathered data, allowing them to monitor the agricultural conditions in real-time. The main key features are Data Visualization in which users can remotely monitor soil moisture levels and receive real-time updates on crop conditions through a web application. Resource Efficiency by automating irrigation, this model optimizes water usage, reducing waste and improving water conservation. This paper not only showcases the integration of IoT technology in agriculture but also serves as a practical and environmentally responsible solution for modern farming practices.

Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality

The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on wine, with a case study focusing on the Denomination of Controlled and Guaranteed Origin Chianti Classico (DOCG), a prime wine-producing region located in Tuscany, between the provinces of Siena and Florence. We first collected a novel dataset, where geographic information as well as wine quality information were collected, using publicly available sources. Using such geographic information retrieved and an unsupervised machine learning approach, we conducted an in-depth examination of pedoclimatic and production data. To collect the whole set of possibly relevant features, we first assessed the region's morphological attributes, including altitude, exposure, and slopes, while pinpointing individual wineries. Subsequently we then calculated crucial viticultural indices such as the Winkler, Huglin, Fregoni, and Freshness Index by utilizing daily temperature records from Chianti Classico, and we further related them to an assessment of wine quality. In addition to this, we designed and distributed a survey conducted among a sample of wineries situated in the Chianti Classico area, obtaining valuable insights into local data. The primary goal of this study is to elucidate the interrelationships between various parameters associated with the region, considering influential factors such as the environment, viticulture, and field operations that significantly impact wine production. By doing so, wineries could potentially unlock the full potential of their resources. In fact, through the unsupervised and correlation analysis we could elucidate the relationships existing between the pedoclimatic parameters of the region, considering the most important factors such as viticulture and field operations, and relate them to wine quality as for instance using the survey data collected. This study represents an unprecedent in the literature, and it could pave the path for future studies focusing on the importance of climatic factors into production and quality of wines.

Integrated Nutrient Management in Wheat (Triticum aestivum L.): An Overview

Wheat is one of the major staple crops in the country in terms of both production and consumption. It is the nation's second-most significant food, after maize, in terms of calories consumed. Nutritious characteristics of wheat farming have been extensively researched and extensively recorded. Research being conducted in several parts of Ethiopia has shown that applying chemical fertilisers to achieve all the necessary nutrients has a detrimental effect on soil health, resulting in yields that are not sustainable. Therefore; there is a need to improve nutrient supply system in terms of integrated nutrient management involving the use of chemical fertilizers in conjunction with organic manures coupled with input through biological processes. However, the impact of important nutrients on crop physiology and their function on growth, quality, yield, and yield components of cereal crops—particularly wheat—are hardly in this level. Above all, the administration of essential nutrients to plants in the right amount and light ratio for a particular soil crop condition plays a crucial role in balanced fertilizer's ability to improve wheat production's yield, quality, and other characteristics. In association with this, research on integrated nutrient management in wheat and its effect on growth, yield, yield components and quality parameters are significance.

Enhancing the irrigation water productivity of rice farming: A study on sowing and irrigation practices in Pakistan

AbstractIn Pakistan's arid agricultural landscape, this 4‐year (2020–2023) study on silty loam soil examined the relationships between sowing methods, irrigation practices, rice yield and water productivity. Using a randomized split block design, no tillage (NT) with alternate wetting and drying (AWD) and a furrow‐irrigated narrow bed (NB) emerged as being consistently competitive, showing potential for sustainable rice cultivation. Key findings indicate that AWD significantly enhances water productivity (11–35%) and increases yield (2–10%) compared to conventional practices. Drip‐irrigated wide beds (WBs) conserve substantial amounts of water (73%) but may compromise rice yield (4–47%). This study provides valuable insights into the dynamic outcomes and distinct performance trends of different rice varieties. The results suggest a potential maximum yield increase of 2–22% through optimized practices, and replacing rice may increase yield by 25–231%. Conversely, the selection of improper irrigation schedules and sowing methods for the given soil and crop conditions may lead to a yield reduction of up to 47% in high‐yielding rice varieties. These findings may help to establish a foundation for further research investigating informed decision making for sowing and irrigation methods. The potential scalability of the identified technologies and further in‐depth simulation strategies across diverse agroecological zones in Pakistan holds promise for widespread adoption, enhancing the sustainability of rice farming in the region.

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Detecting Environmental Conditions in Cultivation Lands Using Bionic Devices

Climate change has an impact on crops, fruits, vegetables, and pest infestation, hence agricultural output is a top priority for most nations. As a result, professional growers have the problem of reaching maximum output results, and greenhouses have emerged as an excellent choice for ensuring these results. Farmers can employ innovative technologies inside greenhouses to prevent insect damage to plants and increase indoor growth through climate management. However, in order to manage agricultural fields and greenhouses successfully, farmers must now use Industry 4.0 technology such as robotics, Internet of Things devices, machine learning software, and so on. In this setting, sensor deployment is critical for gathering data and obtaining knowledge to help farmers make decisions. As a practical option for small farms, this research proposes an autonomous robot that drives along greenhouse crop paths with previously specified routes and can collect environmental data provided by a wireless sensor network when the farmer has no prior knowledge of the crop. An unsupervised learning method is used to cluster the optimal, standard, and deficient sectors of a greenhouse in order to identify improper crop development patterns. Finally, a user interface is built to assist farmers in planning the robot's route and distance while gathering sensor data to monitor crop conditions.

How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?

Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where food insecurity is high and agricultural statistics are sparse. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Abstract. Synthetic aperture radar (SAR) has emerged as a promising technology for monitoring crop plant height due to its ability to capture the geometric properties of crops. Radar vegetation index (RVI) has been extensively utilized for qualitative and quantitative remote sensing monitoring of vegetation growth dynamics. However, the combination of crop, growing environment, and temporal dynamics makes crop monitoring data a complex task. Despite the relatively simple underlying mechanisms of this phenomenon, there is still a need for more research to identify specific vegetation structures that correspond to changes in the response of vegetation indices. Building upon this premise, this study utilized a dynamic monitoring model to conduct dynamic monitoring of plant height for three common crops: rice, wheat, and maize. The findings revealed that (1) models developed for specific spatial and temporal scales of particular crop varieties may not accurately predict crop growth in different regions or with different varieties in a timely manner, due to growth variations; (2) these models maintain accuracy over a range of plant heights, such as rice at around 70cm, wheat at around 50cm, and maize at around 150cm; (3) among the three crops, planting density was identified as the main factor influencing the differences in RVI response. This research contributes to our comprehension of the dynamic response of RVI to different growth conditions in crops, and offers valuable insights and references for agricultural monitoring.

Exploring Soil Fertility Variations Under Nephrolepis Biserrata In Multi-Soil Type Of Oil Palm Plantations

Objective: The aim of this study was to analyze the physical and chemical properties of soil under Nephrolepis biserrata as a ground cover crop in oil palm plantations on Ultisols, Spodosols, and Histosols. Theoretical Framework: The research design comprehensively outlines the procedures for conducting the study, including data sources, collection methods, and analysis techniques, ensuring clarity in describing variables and facilitating effective data collection and analysis. Method: The study design used a splitplot with the main plot on soil types (Ultisols, Spodosols, Histosols) and subplots on cover crop conditions (without cover crop, N. biserrata, various cover crops) covering an area of 180 ha. Results: The research findings demonstrate that the use of cover crops significantly improves both the physical and chemical properties of soil in oil palm plantations. Notably, cover crops effectively enhance various chemical properties, such as pH, organic carbon, total nitrogen, phosphorus, and exchangeable cations, across different soil types. Even in Histosols with naturally high nutrient levels, implementing cover crops yields substantial benefits, particularly in pH improvement and nutrient enrichment. Research Implications: N. biserrata demonstrated better results compared to different cover crops. However, it's crucial to carefully select appropriate cover crops to improve soil quality and support the growth of oil palm plantations, considering the specific soil type. Originality/Value: This research, focusing on the physical and chemical properties of soil under N. biserrata in Indonesian oil palm plantations, serves as a valuable reference for companies seeking to select the most suitable ground cover plants tailored to their soil types.

Eyes in the sky on Tigray, Ethiopia - Monitoring the impact of armed conflict on cultivated highlands using satellite imagery

The war in Tigray, Ethiopia has triggered a massive humanitarian crisis, displacing millions. Yet, the impact on cultivated land and local food production remains poorly understood, impeding effective aid. Leveraging Sentinel-2 satellite imagery and a decision tree algorithm with Normalized Difference Vegetation Index (NDVI) time series, we developed a model to map well-cultivated cropland, defined as fields judged by field surveyors to have satisfactory to optimal crop condition in 2021–2022 field observations. Assessing satellite estimated well-cultivated land in highland croplands (> 1200 m a.s.l), we found a net loss of 543 sq. km (95% CI: 81 sq. km) well-cultivated land in highland croplands equivalent to ≈ 8% of the average total surveyed cropland estimate from Central Statistical Agency between 2015 and 2019 (ESS, 2023a) in potential highland cropland. The net loss was positively associated with the density of recorded conflict incidents and sub-regions with high numbers of internally displaced persons (IDPs), consistent with a causal effect of the conflict on cultivated land.Employing a two-way fixed effect model causal analysis with rainfall covariates, we quantified the impact of conflict incidents on cultivated land during the pre-war (2019/20) and in-war (2021) periods. Results indicated a ≈ 6.17 sq. km (SE: 2.06) additional loss per unit increase in conflict incidents during the growing season (June to October), eight times higher than total incidents occurring throughout the entire study period. We estimated the kilocalories lost due to loss of well-cultivated croplands in 2021 could have supported at least 90% of all recorded IDPs in Tigray as of June 2021, discounting for Western Tigray. Our study showcases the utility of satellite data, coupled with local agricultural knowledge, for timely and cost-effective information crucial for aid agencies and long-term rehabilitation initiatives in smallholder farming contexts.

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Evaluation of the Plant Growth Regulator SPGP4 in Agricultural Crops: A Case Study in Oaxaca, México

The search for new plant growth regulators is a cornerstone of agricultural research; however, laboratory studies rarely go on to be evaluated in the field. This is because greater production is required, as well as longer studies. Particularly, brassinosteroids present these difficulties, and although they have been evaluated in crops with good results, their high production cost gives rise to the search for new alternatives. 22-Oxocholestanes such as SPGP4, previously used in silico and in vitro studies, have shown great potential, so their evaluation in crops grown from native seeds from the study region becomes of interest. Based on these data, SPGP4 was evaluated under crop conditions in three agricultural plots located on the Isthmus of Tehuantepec region, Oaxaca, México. The seeds were treated with a 0.5 mg/L aqueous solution of the 22-Oxocholestane compound SPGP4 by imbibition one night before sown. Later, 45 days after sowing, a solution of 0.5 mg/L at a rate of 200 L per hectare was applied. At the production level, the bean harvest showed an increase in the range of 21.0–38.1%, and the corn harvest increased between 22 and 32%. In addition, the latter also demonstrated an increase in biomass production, given the increase in diameter and height observed in the corn plant. This indicates that SPGP4 functions as a regulator of plant growth at the crop level to increase both seed and biomass production.

ЦИФРОВЫЕ ИННОВАЦИИ В ЗЕРНОВОМ ПРОИЗВОДСТВЕ: МЕТОДОЛОГИЧЕСКИЕ ПРИНЦИПЫ ИСПОЛЬЗОВАНИЯ ТЕХНОЛОГИЙ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

The paper discusses an innovative approach to monitoring grain ecosystems, based on the use of neural network technologies. A classification of the tasks of monitoring the phytosanitary condition of crops in a grain field has been carried out, and the corresponding intellectualization tools have been identified. The main attention is paid to the problems of detection, classification and development of diseases in crops, for the effective solution of which it is proposed to use computer vision methods, including a complex of convolutional architectures GoogleNet, DenseNet, U-Net, which have shown high performance in classification and segmentation problems on test samples of images of wheat diseases, obtained as a result of three years of field experiments in the Krasnodar Region. The results of the study show that the use of neural network methods in the process of monitoring grain ecosystems contributes to the effective solution of complex problems associated with diagnostic procedures, allowing to reduce the level of uncertainty in the decision-making process, which is especially important under the influence of environmental factors with a high level of randomness and variability. The main barrier to the intellectualization of production processes is the lack of methodology for working with artificial intelligence, big data and other digital technologies at different levels of management in the agricultural sector of the economy, which affects not only issues of technical implementation and implementation of artificial intelligence, but also organizational aspects, including work with data, staffing the intellectualization process, information infrastructure, defining the roles and responsibilities of participants in the process, as well as the integration of intelligent solutions with the agricultural solutions module of the national platform “Digital Agriculture”.

Advancements in Biofertilizers for Pulse Crops: From Single-Strain Inoculants to Microbiome-Based Technologies

The application of biofertilizers in pulse crop agriculture has garnered significant attention as an eco-friendly and sustainable alternative to conventional chemical fertilizers. This comprehensive review aims to elucidate the progression of biofertilizer technologies from traditional single-strain inoculants to contemporary microbiome-based strategies. Pulse crops, such as beans, lentils, chickpeas, and peas, are pivotal in global agriculture due to their high protein content, nitrogen-fixing ability, and role in improving soil health. However, optimizing their productivity necessitates effective nutrient management, where biofertilizers play a crucial role. Initially, biofertilizer development concentrated on single-strain inoculants, predominantly Rhizobium species, which establish symbiotic relationships with leguminous plants to enhance nitrogen fixation and improve crop yields. Despite their proven benefits, single-strain inoculants often exhibit variable performance influenced by soil conditions, climatic factors, and interactions with indigenous soil microbiota. This variability highlights the need for more consistent and resilient biofertilizer solutions. Recent advances in microbiome research have paved the way for the development of microbiome-based biofertilizers. These advanced formulations leverage the synergistic interactions among diverse microbial communities, including bacteria, fungi, and archaea, to provide a more comprehensive and stable enhancement of soil fertility and plant growth. Microbiome-based approaches recognize the complexity of plant-microbe-soil interactions, offering improved nutrient cycling, increased plant stress resilience, and better adaptation to various environmental conditions. The benefits of microbiome-based biofertilizers are manifold, encompassing enhanced nutrient availability, improved plant health, and sustainable soil management. However, their development and application pose several challenges, including understanding the intricate dynamics of microbiome interactions, developing precise formulation and application methods, and navigating regulatory landscapes. Addressing these challenges requires multidisciplinary research and innovation, integrating genomics, metagenomics, and advanced data analytics to unravel the complexities of soil microbiomes. This includes the potential for personalized biofertilizer formulations tailored to specific soil and crop conditions, enhancing the efficacy and scalability of biofertilizer technologies.