Modern Agriculture Research Articles

Econometric Analysis of Factors Affecting Access to Market by Yam Farmers in Benue State

It is evident that in developing countries, agriculture serves as a foundation for economic activities. It plays an active role in determining the wealth, social and political system of a society. Therefore, policies and programmes as well as research and development in the sector is necessary for increased productivity and marketing. This study examines the variables influencing yam farmers’ access to markets in Benue State. The level to which the produce intended for sale the previous season was actually sold was used to determine whether or not the market was accessible. Data were collected with the use of a structured questionnaire from three local governments in Sankera Zone, the dominant yam producing zone of Benue state. The results of the logistic regression analysis showed the variables that affected the likelihood that a farmer in the study area would have easy access to markets. Gender, educational attainment, prior farming experience, vehicle ownership, and market space all showed positive and had a significant impact on the likelihood that a farmer would have simple access to the market. However, the age of the household and size had a negative effect, and were statistically significant. A farmer training programme is strongly recommended to equip farmers with modern farming techniques. Additionally, the establishment of infrastructural facilities, market boards, and yam processing factories were recommended to support their endeavors.

Co-composting of tail vegetable with flue-cured tobacco leaves: analysis of nitrogen transformation and estimation as a seed germination agent for halophyte.

Resource utilization of tail vegetables has raised increasing concerns in the modern agriculture. However, the effect and related mechanisms of flue-cured tobacco leaves on the product quality, phytotoxicity and bacterially-mediated nitrogen (N) transformation process of tail vegetable composting were poorly understood. Amendments of high-dosed (5% and 10% w/w) tobacco leaves into the compost accelerated the heating process, prolonged the time of thermophilic stage, increased the peak temperature, thereby improving maturity and shortening composting duration. The tobacco leaf amendments at the 10% (w/w) increased the N conservation (TN and NH4-N content) of compost, due to the supply of N-containing nutrient and promotion of organic matter degradation by tobacco leaves. Besides, tobacco leaf amendments promoted the seed germination and root development of wild soybean, exhibiting the feasibility of composting product for promoting the growth of salt-tolerant plants, but no dose-dependent effect was found for tobacco leaf amendments. Addition of high dosed (5% and 10% w/w) tobacco leaves shifted the bacterial community towards lignocellulosic and N-fixing bacteria, contributing to increasing the compost maturity and N retention. PICRUSt 2 functional prediction revealed that N-related bacterial metabolism (i.e., hydroxylamine oxidation and denitrifying process) was enhanced in the tobacco leaf treatments, which contributed to N retention and elevated nutrient quality of composting. To the best knowledge, this was the first study to explore the effect of tobacco waste additives on the nutrient transformation and halophyte growth promotion of organic waste composting. These findings will deepen the understanding of microbially-mediated N transformation and composting processes involving flue-cured tobacco leaves.

Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand-wrist-forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity.

Battery-Operated Power Weeding Machines: Advancements, Challenges, and Future Prospects

In recent years, significant advancements have been made in the development and adoption of battery-operated power weeding machines, offering a sustainable and efficient alternative to traditional weed management methods. Previous studies have highlighted several critical benefits of these machines, particularly in reducing labor costs and minimizing environmental impact. Battery-powered weeders have been shown to significantly cut down on manual labor requirements, thus reducing the physical strain on farmers and decreasing the reliance on hired labor. This reduction in operational costs makes them an economically viable option for small and medium-sized farms. Furthermore, the environmental advantages of these machines are notable. Unlike chemical herbicides, which pose risks of soil and water contamination and potential health hazards, battery-operated weeders offer a clean alternative, eliminating the need for harmful chemicals. This aligns with the growing demand for environmentally sustainable agricultural practices. Technological advancements have further enhanced the efficiency and usability of these machines. Modern battery-powered weeders are increasingly equipped with lithium-ion batteries that provide longer operation times, precision sensors for accurate weed targeting, and automated control systems that simplify operation. These innovations have not only improved the effectiveness of weed management but have also made these machines more accessible and user-friendly for farmers. Field trials and studies conducted in various agricultural settings have consistently demonstrated the practicality and reliability of battery-operated weeders, making them a transformative solution in modern agriculture. The integration of these machines into farming practices has been shown to improve productivity, reduce costs, and promote sustainability, solidifying their role as a critical innovation in the future of agriculture. This review article aims to build upon these findings by providing a detailed examination of the design, development, and performance of battery-operated weeding machines, and exploring their broader applications in diverse agricultural environments.

A COMPREHENSIVE STUDY ON SOCIO-ECONOMIC ANALYSIS OF POTATO GROWERS IN KANGRA DISTRICT OF HIMACHAL PRADESH

This study investigates the socio-economic status of potato farmers in the Kangra district of Himachal Pradesh. This district was chosen specifically because it has a substantial area under potato cultivation. The required information was gathered from two potato-growing blocks in the district. The study revealed that the majority of household heads (57%) are between the ages of 16 and 40, with an average family size of five to seven people. Agriculture and livestock rearing emerge as the primary sources of income for most farms, with an average farm size of 1.76 ha, 74% of which are irrigated. During the Kharif season, vegetable crops are dominant, while potatoes are important during the Rabi season, with an average of 21.23 ha per farm. Potato productivity varies, with large farms producing 253.75 q/ha versus 246.43 q/ha on small farms. Agriculture-related earnings, or farm earnings, account for 45.2% of total farm income, with potatoes accounting for 8%, while non-farm income accounts for ~26.5%. Future research topics include investigating the impact of climate change on potato farming and evaluating the effectiveness of agricultural policies in terms of income and productivity. Further research could look into the effects of modern farming techniques on sustainability and profitability, potentially providing insights into improving agricultural practices in the region.

Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations

AbstractAgriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.

Smart-Agro: Enhancing Crop Management with Agribot

The Agri-Bot robotic system indeed characterizes a substantial advancement in modern agriculture, offering a multifaceted solution for monitoring and managing agricultural environments. By integrating various Arduino-based sensors and motor drivers, it provides a comprehensive toolkit for farmers to effectively oversee their crops' health and optimize resource usage. The inclusion of pH and moisture sensors enables real-time monitoring of soil conditions, allowing farmers to adjust irrigation and fertilizer application precisely according to the plants' needs. Additionally, the DHT11 sensor offers insights into ambient conditions crucial for plant growth, such as temperature and humidity, facilitating informed decision-making. The incorporation of the L298 motor driver further enhances the system's capabilities by enabling automation of tasks like irrigation and seed sowing with precision and efficiency. This integration of robotics and sensor technology not only streamlines agricultural processes but also empowers farmers with data-driven insights to optimize crop growth and sustainability.

A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model

The development of a real-time online system for rapid and non-destructive identification of seed varieties can significantly improve production efficiency in modern agriculture. Near-infrared spectroscopy technology has become one of the commonly used techniques in seed variety identification due to its fast and non-destructive characteristics. However, existing convolutional neural networks are difficult to reflect the complex nonlinear relationships of the near-infrared (NIR) spectrum, resulting in poor modeling performance, and their high model complexity is not conducive to real-time online identification tasks. Therefore, this study proposes a maize seed variety identification method using near-infrared spectroscopy technology and lightweight deep learning network (BAC-DenseNet). First, a total of 750 samples from 5 different types of maize seeds were taken as the research object. The spectral data were pre-processing using the SGD2-SNV, and the identification accuracy was improved by an average of 15.78 %. Then, the attraction–repulsion optimization algorithm combined with Laplacian Eigenmaps (AROA-LE) was used to perform dimension reduction on the pre-processed data, and the dimensionality was reduced from 1845 to 66. Finally, a lightweight deep learning network model (BAC-DenseNet) was constructed based on DenseNet-121 network with layer pruning and the introduction of batch channel normalization (BCN), self-attention and convolution mixed module (ACmix) and convolutional block attention module (CBAM). The experimental results show that the proposed BAC-DenseNet model has an identification accuracy of 99.33 %. Compared with the original network and seven other classical deep learning models, the proposed method has an average improvement of 2.83 %, 3.52 %, and 3.47 % in accuracy, Kappa, and MCC, respectively. Meanwhile, Params, Size, and FLOPs decreased by an average of 9.09 M, 35.08 MB, and 88.66 M, respectively. This method offered high accuracy and reliability in maize seed variety identification, which can provide qualitative indicators for the breeding, planting, and management of maize seed varieties. This study can provide a reference method for variety identification of other agricultural products.

Problems and Countermeasures of Apple Agricultural Products Futures Market in China

In recent years, the improvement of quality and efficiency of national economy and agricultural modernization have directly promoted the innovative development of apple industry in China. At the end of 2017, the first Apple futures in China was born, and then the scale of Apple trading market continued to expand, and the standardization of Apple futures trading market increased year by year, which directly promoted the adjustment and upgrading of Apple industry. However, the development of Apple futures market lags behind the current situation of spot market development, and the imperfect risk management system, low internationalization level of Apple futures, imperfect laws and regulations of Apple futures market and chaos of market speculators still need to be solved urgently. In order to promote the rapid development of China's apple futures market, it is important to improve the trading system of bulk agricultural products; Strengthen the risk management mechanism; Strengthen international market cooperation and exchanges; Revise and improve the relevant laws and regulations of the Apple Futures Market and improve the level of market standardization; Take measures to limit speculation.

A COMPARATIVE ANALYSIS OF LAND FRAGMENTATION AND CONSOLIDATION PRACTICES, POLICY RESPONSES IN TÜRKİYE AND KYRGYZSTAN

Understanding agricultural land dynamics is imperative for sustainable development. This research presents a comparative analysis of land fragmentation and consolidation practices in Türkiye and Kyrgyzstan. Land fragmentation, a global issue, hinders agricultural modernization due to small, scattered parcels. Factors such as inheritance laws, population pressure, and economic shifts contribute to fragmentation. Land consolidation, a solution advocated by FAO and FIG, aims to redistribute land, enhance productivity, and achieve public objectives. While Türkiye demonstrates successful land consolidation efforts, Kyrgyzstan lacks legislation and institutional capacity, impeding progress. Recommendations include tailored legislation, institutional strengthening, and international collaboration. Türkiye's experience shows significant reductions in parcel numbers and increased access to resources. Addressing fragmentation requires simultaneous consolidation and land banking initiatives. By implementing these recommendations, both countries can overcome fragmentation challenges and foster sustainable agricultural development.

Robotic solutions for precision agriculture

The robotic system is a key component in modern agriculture. The article aims to expand robotic solutions for precision agriculture and improve productivity, resource utilization, decision-making, sustainability, and farmworker safety. It aims to automate repetitive tasks, gather real-time data, promote sustainable practices, and reduce risks for farmworkers. They offer numerous potential solutions to issues related to the growing global population, changing demographics, and economic status. This article investigates how robotic systems can be significant to precision agriculture. Traditional farming is facing issues such as climate change, resource depletion, labour shortage, etc. The use of robotic systems makes precision agriculture achievable and it provides a sustainable solution. This study examined the importance of robotics in agricultural processes such as planting, seeding, weeding, and harvesting. The potential benefits of robotic solutions, such as increased efficiency, reduced labour costs, and improved crop yield, are explored. The article identifies key challenges and opportunities associated with robotic implementation in agriculture. The research aids in creative effective agriculture techniques by imaging the future use of robotics in agriculture.

Analysis of the Economic Feasibility of Using Modern Agricultural Machinery in Oil Palm Plantations in South Sumatra

This study investigates the economic feasibility of using modern agricultural machinery in oil palm plantations in South Sumatra. A quantitative approach was employed, involving 40 samples collected through a structured questionnaire utilizing a Likert scale from 1 to 5. The data were analyzed using SPSS version 26, focusing on key economic indicators such as production efficiency, cost savings, and profitability. The results revealed strong positive and significant relationships between the use of modern machinery and all three economic indicators. The findings suggest that modern agricultural machinery significantly enhances economic performance in oil palm plantations, making it a viable investment for improving productivity and profitability. These insights provide valuable guidance for plantation owners and policymakers in promoting the adoption of advanced agricultural technology in the region.

Application of nano-urea in conventional flood-irrigated Boro rice in Bangladesh and nitrogen losses investigation

Bangladesh stands third in global rice production while complete modernization of rice production is not fully enforced. The boon of nano agriculture might circumvent the challenge of increasing the yield with minimal ecological damage. Nanofertilizer might be one of the solutions to address the problem of modern agriculture confronting environmental hazards owing to the excessive use of synthetic fertilizers by farmers in Bangladesh. We synthesized nanourea by chemical co-precipitation (CP) and hydrothermal (HT) methods in an attempt to develop environmentally friendly nanofertilizers. We characterized the nanourea and confirmed the functionalization of nanohydroxyapatite (nHAP) with urea by scanning transmission electron microscopy (STEM)/EDS mapping. The CP method produced particle dimensions of 45.62 nm for length and 14.16 nm for width. In comparison, the readings obtained through the HT method were around 74.69 nm and 20.44 nm for length and width, respectively. The field application of nanourea demonstrated impressive results, indicating a significant relationship between the particle size of nanourea and its impact on several agricultural factors. The grain yield using traditional synthetic fertilizer (urea) ranged from 6.47 to 6.52 t ha−1 with a very low NUE of 35.8–36.34 %. Contrarily, the grain yield was found from 6.52 to 6.84 t ha−1 and the obtained NUE ranged from 57.58 to 71.0 % using nanourea of the same concentration calibrated with traditional urea by two methods. Additionally, nanourea treatments having 25 % less nitrogen (N) provided higher total N (TN) in grain suggesting possible nutritional enrichment while checking the yield penalty and substantial increase in N use efficiency (NUE). However, further upscaling of this research on a field scale is necessary to confirm the findings.

Greenhouse farming: Hydroponic vertical farming- Internet of Things (IOT) Technologies: An updated review

This review paper of literature highlights the importance of greenhouse urban farming technology, hydroponics, aeroponics, aquaponics, vertical farming and applications of Internet of Things (IOT) technologies. The greenhouse farming is a well known modern agriculture technology for optimal plant growth. Hydroponics, a soilless cultivation technique using nutrient solutions under controlled conditions, is used for growing vegetables, high-value crops, and flowers. Vertical farming is a popular trend in hydroponics that involves stacking multiple layers of plants in a vertical arrangement. This farming method saves space, reduces water usage, and increases yields per square foot of growing area. In comparison to conventional greenhouse cultivation, hydroponics requires less fertilizer, pesticides, and water due to the precise control over their distribution. The term “Internet of Things (IOT)” is a system of interconnected computing devices, sensors, objects, microcontrollers, and cloud servers that can transmit data across a network and control other devices remotely without human intervention. A better management of nutrient solution in hydroponic systems requires optimum pH, electrical conductivity (EC), or ions concentration. The three main greenhouse farming gases (GHGs) emissions in hydroponics hi-tech urban farming are nitrous oxide, methane and carbon dioxide (CO2). To measure the levels of CO2 in hydroponics, the more intermediate to advanced grower can use a CO2 monitoring and controllers system. Hydroponic vertical farming is in infancy in India. Although hydroponic vertical farming units for production of crops like strawberry, lettuce and other leafy vegetables, foliage and flowers are functioning in major metros of India. However, the organized hydroponic vertical farms for production of food crops are not available in India. Therefore, only a few successful hydroponic vertical farms have been built in India mainly due to the initial high price tag on construction and the cost of maintaining them afterwards. Another disadvantage is carbon footprint of hydroponic vertical farming is very high, and discourages its applications.

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI).

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

IoT enhanced deep water culture hydroponic system for optimizing Chinese celery yield and economic evaluation

This research investigates the integration of a deep-water culture hydroponic system with Internet of Things (IoT) technology to enhance Chinese celery cultivation. Four experimental setups were implemented in 2 × 6 meter greenhouses, each replicated thrice: combined light and temperature control, temperature control only, light control only, and natural conditions. The 45-day experiment focused on extended light exposure (660 nm, 6:00-11:00 p.m.) and temperature regulation (fog sprayer above 35°C). An IoT-based system using Blynk and ESP32 maintained optimal water conditions, with monthly sensor calibration and regular maintenance ensuring reliability. Data collected every 5-7 days were analyzed using the Friedman test. Results showed the greenhouse with both controls achieved a 13.91% higher yield than natural conditions, with temperature regulation being more critical. Economic analysis indicated 14% higher profitability for the controlled greenhouse, with a 27-month breakeven point. Potential challenges like sensor failures were addressed through redundancy measures. This study highlights IoT-controlled hydroponics' potential for enhancing agricultural productivity and sustainability while acknowledging maintenance needs and scalability challenges. The findings provide valuable insights into modern farming practices, demonstrating improved yield and economic viability through precise environmental control and automated management in hydroponic systems.

Selection of New Energy Development Models in Rural Jilin Province under Different Situations

Jilin Province’s vast rural areas are endowed with abundant new energy resources, which provide a fundamental condition for the development of new energy in rural Jilin. This paper will explore the various possibilities of new energy development in rural Jilin under the background of grid infrastructure construction, the deepening of electricity market reform, the modernization of agriculture and rural areas, and the transformation of energy. It will draw conclusions on the reasonable development models of new energy in rural Jilin under different scenarios, providing a reference for the development of new energy in rural Jilin and rural revitalization.

AUTOMATIC SPRINKLER SYSTEM USING ARDUINO

This report investigates the implementation and benefits of an Automatic Sprinkler System driven by the Arduino Uno microcontroller in agricultural irrigation. The primary aim is to elucidate the functionality and practicality of this automated system in enhancing water efficiency and crop productivity. Through accessible language and technical explanations, the report explores the system's design, operation, and integration with sensors for optimal performance. Emphasis is placed on the system's ability to provide precise watering, customizable scheduling, and water conservation, thereby addressing critical challenges in modern agriculture. By examining the technical aspects and real-world implications, this report underscores the transformative potential of automated irrigation systems in promoting sustainable farming practices.. Keyword: Arduino Uno, IoT, microcontroller, moisture sensor, sensors, wireless module

Research of real-time corn yield monitoring system with DNN-based prediction model.

The real-time monitoring of corn yield by a combine harvester is a critical data source for constructing the yield histogram, which significantly benefits precision management and decision-making in modern precision agriculture. While widely used, the current photoelectric sensor-based yield monitoring method has limitations. It detects the corn height on each scraper and calculates the yield through a geometric formula. However, it neglects the noticeable difference in the corn stacking patterns affected by factors such as feeding volume, terrain, and driving speed. This oversight often results in low accuracy and poor stability in the prediction of corn yield, highlighting the need for a more advanced approach. To resolve this, we employ EDEM discrete element simulation to demonstrate the large difference of corn stacking patterns on the scraper of the elevator corresponding to feeding volume. Then, we develop a real-time monitoring system on our self-developed double elevator testing rig for carrying out a composite dataset for training three machine learning algorithm-based models, namely Deep Neural Networks (DNN), Gradient Boosting Machine (GBM), and Random Forest (RF). Importantly, these models have undergone rigorous validation under various feeding volumes, ensuring their robustness and reliability. The auxiliary elevator speed is meticulously set at 150r/min, 225r/min, and 450r/min, providing a comprehensive performance assessment. The results denote that the DNN model performs best and is stable, with a coefficient of determination (R2) of 0.998, root mean square error (RMSE) of 0.526, and mean absolute error (MAE) of 0.425. The paper also performs field experiments to test the proposed three prediction models and the system. The results also denote the DNN-based prediction model's best performance for the lowest relative error of 2.29% and the highest average accuracy of 97.85%. Consequently, the proposed real-time corn yield monitoring system achieves high accuracy and reliability for the combine harvester applications.

Rapid prediction and visualization of safe moisture content in alfalfa seeds based on multispectral imaging technology

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.