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Precision maize stand analysis using remote sensing methods: plant density measurement with spectral data integration
Views:213The aim of this study was to use remote sensing with a drone equipped with a multispectral camera to take a stand survey of maize after the phenological stage of emergence, and to count the number of emerged plants and determine its accuracy. Our investigations were carried out at the University of Debrecen, Látókép Production Experimental Station in a sowing date long-term experiment. In the 2024 growing season, Sowing Date I was on 4 April and Sowing Date II on 12 April. The same maize hybrids with 8-8 different genotypes were used for each sowing date. There is a strong correlation between number of plants/plot and number of plants/rowx2 for the two plant density measurements presented in this paper, with an r value of 0.977*** (p < 0.001). Among the plant density and NDVI values, the correlation between number of plants/rowx2 at the second measurement time (July 4) was significant at r=-0.418***. The analysis of the relationship between number of rows and yield showed that the hybrids included in the study compensated well for differences in number of rows due to sowing or emergence and this did not translate into an increase or decrease in yield. By using the plant density count method and results to identify emergence imbalances, farmers can correct their crop stand management strategies in a timely manner. Knowing the exact number of plants can also be important for subsequent agrotechnical decisions.
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Predicting maize yield with a multilayer perceptron (MLP) model using multivariate field data
Views:216This study presents the findings of a multi-year maize field trial conducted on experimental plots between 2017 and 2019, focusing on the application of machine learning techniques to enhance yield prediction accuracy. A multilayer perceptron (MLP) neural network was employed to model the effects of agronomic treatments, environmental variation, and compositional traits. Six distinct modeling scenarios were developed to explore different combinations of input variables, with the grain yield of maize serving as the sole output parameter. These scenarios range from treatment-only models to those incorporating detailed quality and compositional data. The primary objective was to evaluate how well MLP models can capture the complex, nonlinear relationships influencing yield under varying conditions. The findings provide valuable insight into the role of machine learning in supporting decision-making for sustainable crop production, especially under diverse technological and environmental settings. The approach demonstrated here offers a foundation for more adaptable, data-driven strategies in agronomic optimization.
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Stimulating maize growth under different water regimes through foliar application of micronutrients
Views:32Drought and nutrient stress constrain maize growth, production and productivity. Precision agricultural tools such as foliar fertilisation and irrigation enhance maize growth through mitigation of stresses at different growth stages. The study conducted at Látókép Crop Production Experimental Site, University of Debrecen, Hungary during year 2023, assessed the physiological and growth performance of two maize hybrids exposed to foliar fertilisation under precision drip irrigation and non-irrigated conditions. Foliar fertilizers composed of; nitrogen (10 g/l), zinc (8 g/l), K2O (8.5 g/l), P2O5 (0.83 g/l), and S (8.93 g/l). Data on plant height, leaf area index (LAI), normalized difference vegetation index (NDVI), and relative chlorophyll content (SPAD) were collected at the V12, R1, R4, and R6 analysed using a T-test in Genstat software (12th edition). Results showed significant increases in all parameters at the V12, R1, and R4, but NDVI, LAI, and SPAD declined at R6 under both irrigation and non-irrigated conditions. Foliar fertilisation of the two maize hybrids under precision drip irrigation conditions showed that NDVI and plant height had a significant difference (p < 0.05) while no significance difference was noted for SPAD and leaf area index. The percentage LAI due to foliar fertilisation under irrigated conditions was 16.86% and 18.39% for FAO490 and FAO290 respectively however the effect was slightly higher for FAO290 than FAO490. FAO490 and FAO290 hybrids recorded a 23.45% and 10.05% foliar fertilisation effect respectively over control under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions.
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Identifying maize yield drivers using statistical analysis and multilayer perceptron modelling
Views:23Maize yield formation is jointly determined by crop-year conditions, water availability, nutrient management, and their interactions, which may include complex nonlinear relationships. This study aimed to identify the principal agronomic and physiological variables associated with maize yield using classical statistical analyses complemented by multilayer perceptron modelling. Field data collected in Debrecen, Hungary, during 2024–2025 included crop year, irrigation, fertilizer treatment, phenological stage, SPAD chlorophyll readings, and grain yield. Pearson correlation and linear regression quantified individual relationships, while four MLP scenarios evaluated combined predictive effects. Fertilizer was positively associated with yield in both years and irrigation regimes, with the strongest relationship under irrigation in 2025 (r = 0.770; R² = 0.594). The SPAD–yield relationship generally strengthened during crop development, reaching its maximum at R3 under irrigation in 2025 (r = 0.916; R² = 0.839). The best-performing MLP scenario included year, fertilizer, and irrigation, confirming their central role in yield prediction and formation.
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The role of sensor technology in sustainable and efficient agricultural production - Review
Views:231Agricultural sensor technology has become a cornerstone of modern precision farming over the past two decades. Our research focused on the applications of remote and proximal sensing technologies, with an emphasis on satellite, drone-based, soil-embedded, and plant-mounted sensors. The study analyzed the presence of sensors in scientific publications and the evolution of research trends. The results showed that the number of scientific publications on sensor technology has grown exponentially, particularly in the last decade, reflecting increasing scientific and practical interest in the field. It was found that modern advancements, such as nanotechnology, have significantly contributed to reducing sensor size and enhancing their sensitivity, thereby supporting sustainable agricultural practices. Sensor applications enable the optimization of water, nutrient, and energy use, contributing to agricultural sustainability. Our research highlighted the importance of sensor technology in improving production efficiency and addressing global agricultural challenges.