AgriEngineering

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil’s physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (−0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices. Full article

Effective water and soil management is crucial for crop productivity, particularly in rice cultivation, where poor soil quality and water scarcity pose challenges. The response of deeper-rooted rice grown in soils amended with different soil amendments (SAs) to Internet of Things (IoT)-managed alternate wetting and drying (AWD) irrigations remains undetermined. This study explores the effects of various SAs on DRO-1 IR64 rice plants under IoT-based soil moisture monitoring of AWD irrigation. A greenhouse experiment executed at the Tokyo University of Agriculture assessed two water management regimes—continuous flooding (CF) and AWD—alongside six types of SAs: vermicompost and peat moss (S + VC + PM), spirulina powder (S + SPP), gypsum (S + GS), rice husk biochar (S + RHB), zeolite (S + ZL), and soil without amendment (S + WA). Soil water content was continuously monitored at 10 cm depth using TEROS 10 probes, with data logged via a ZL6 device and managed through the ZENTRA Cloud application (METER GROUP Company). Under AWD conditions, VC + PM showed the greatest decline in volumetric water content due to enhanced root development and water uptake. In contrast, SPP and ZL maintained consistent water levels. Organic amendments like VC + PM improved soil properties and grain yield, while AWD with ZL and GS optimized water use. Strong associations exist between root traits, biomass, and grain yield. These findings highlight the benefits of integrating SAs for improved productivity in drought-prone rice systems. Full article

Stable pear cultivation relies on cross-pollination, which typically depends on insects or wind. However, natural pollination is often inconsistent due to environmental factors such as temperature and humidity. To ensure reliable fruit set, artificial pollination methods such as wind-powered pollen sprayers are widely used. While effective, these methods require significant labor and operational costs, highlighting the need for a more efficient alternative. To address this issue, this study aims to develop a fully automated drone-based pollination system that integrates Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs). The system is designed to perform artificial pollination while maintaining conventional pear cultivation practices. Demonstration experiments were conducted to evaluate the system’s effectiveness. Results showed that drone pollination achieved a fruit set rate comparable to conventional methods, confirming its feasibility as a labor-saving alternative. This study establishes a practical drone pollination system that eliminates the need for wind, insects, or human labor. By maintaining traditional cultivation practices while improving efficiency, this technology offers a promising solution for sustainable pear production. Full article

Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GI_w), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GI_w only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GI_w. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology. Full article

In agricultural planting, early plant classification is an indicator of crop health and growth. In order to accurately classify early plants, this paper proposes a classification method combining a dual attention mechanism and multi-scale module. Firstly, the ECA module (Efficient channel attention) is added to enhance the attention of the network to plants and suppress irrelevant background noise; secondly, the MSFN (Multi-scale Feedforward Network) module is embedded to improve the ability to capture complex data features. Finally, CA (Channel attention) is added to further emphasize the extracted features, thus enhancing the discrimination ability and improving the accuracy of the model. The experimental results show an accuracy of 93.20%, precision of 94.53%, recall of 93.27%, and an F1 score of 93.39%. This study can realize the classification of early plants, and effectively distinguish crops from weeds, which is helpful to identify and realize accurate weeding, thus promoting the intelligent and modern process of agricultural production. Full article

The mapping and monitoring of rice fields on a large scale using medium and high spatial resolution data (<10 m) is essential for efficient agricultural management and food security. However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. In this sense, the objective of this study was to propose a pipeline to characterize and classify three different irrigated rice-producing regions in the state of Santa Catarina, Brazil. To achieve this, we used Sentinel-1 Synthetic Aperture Radar (SAR) polarizations and Sentinel-2 optical multispectral spectral bands along with multiple time series indices. The processing of input data and exploratory analysis were performed using a clustering algorithm based on Dynamic Time Warping (DTW), with K-means applied to the time series. For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. Among the tested classification models, the SVM with Sentinel-1 and Sentinel-2 data yielded the highest accuracy (IoU: 0.807; Dice: 0.885), while CART and GTBoost had the lowest performance. Omission errors were reduced below 10% in most models when using both sensors, but commission errors remained above 15%, especially for patches in which rice fields represent less than 10% of area. These findings highlight the effectiveness of our proposed feature selection and classification pipeline for improving the generalization of irrigated rice mapping in large and diverse regions. Full article

The application of machine learning techniques to determine bioparameters, such as the leaf area index (LAI) and chlorophyll content, has shown significant potential, particularly with the use of unmanned aerial vehicles (UAVs). This study evaluated the use of RGB images obtained from UAVs to estimate bioparameters in sesame crops, utilizing machine learning techniques and data selection methods. The experiment was conducted at the Federal Rural University of Pernambuco and involved using a portable AccuPAR ceptometer to measure the LAI and spectrophotometry to determine photosynthetic pigments. Field images were captured using a DJI Mavic 2 Enterprise Dual remotely piloted aircraft equipped with RGB and thermal cameras. To manage the high dimensionality of the data, CRITIC and Pearson correlation methods were applied to select the most relevant indices for the XGBoost model. The data were divided into training, testing, and validation sets to ensure model generalization, with performance assessed using the R², MAE, and RMSE metrics. XGBoost effectively estimated the LAI, chlorophyll a, total chlorophyll, and carotenoids (R² > 0.7) but had limited performance for chlorophyll b. Pearson correlation was found to be the most effective data selection method for the algorithm. Full article

In recent years, the agriculture sector has undergone a significant digital transformation, integrating artificial intelligence (AI) technologies to harness and analyze the growing volume of data from diverse sources. Machine learning (ML), a powerful branch of AI, has emerged as an essential tool for developing knowledge-based agricultural systems. Grapevine red blotch disease (GRBD) and grapevine leafroll disease (GLD) are viral infections that severely impact grapevine productivity and longevity, leading to considerable economic losses worldwide. Conventional diagnostic methods for these diseases are costly and time-consuming. To address this, ML-based technologies have been increasingly adopted by researchers for early detection by analyzing the foliar symptoms linked to viral infections. This study focused on detecting GRBD and GLD symptoms using Convolutional Neural Networks (CNNs) in computer vision. YOLOv5 outperformed the other deep learning (DL) models tested, such as YOLOv3, YOLOv8, and ResNet-50, where it achieved 95.36% Precision, 95.77% Recall, and an F1-score of 95.56%. These metrics underscore the model’s effectiveness at accurately classifying grapevine leaves with and without GRBD and/or GLD symptoms. Furthermore, benchmarking was performed with two edge computer devices, where Jetson NANO obtained the best cost–benefit performance. The findings support YOLOv5 as a reliable tool for early diagnosis, offering potential economic benefits for large-scale agricultural monitoring. Full article

Resolving the genetic relationships between cultivars is one of the objectives of research in viticulture. To this end, both DNA markers and morphological analysis help to identify synonyms and homonyms and to determine the degree of relatedness between cultivars. Results of genetic analysis using single sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) point to Hebén as the female progenitor of many of the cultivars currently used in viticulture. Here, seed shape is compared between Hebén and genetically related cultivars. An average silhouette derived from seeds of Hebén was used as a model, and the comparisons were made visually and quantitatively by calculation of J-index values (percent similarity of the seeds and the model). Quantification of seed shape by J-index confirms the data of DNA markers supporting different levels of conservation of maternal seed shape in the varieties. Other seed morphological measurements help to explain the basis of the differences in shape between Hebén, genetically related groups and the external group of unrelated cultivars. Curvature analysis in seeds silhouettes confirms the relationship between Hebén and other cultivars and supports the utility of this technique in the analysis of parental relationships. Full article

The sugarcane cultivation has used heavy machinery on a large scale, which causes soil compaction. The minimum tillage has been used to reduce the traffic of machines on the crop, but there is a lack of appropriate tools for the implementation of this technique, especially in sugarcane areas. The University of Campinas—UNICAMP developed a conservation soil tillage tool called “Rotary paraplow”, the idea was to join the concepts of a vertical milling cutter with the paraplow, which is a tool for subsoiling without inversion of soil. The rotary paraplow is a conservationist tillage because it mobilizes only the planting line with little disturbance of the soil surface and does the tillage with the straw in the area. These conditions make this study pioneering in nature, by proposing an equipment developed to address these issues as an innovation in the agricultural machinery market. We sought to evaluate soil tillage using rotary paraplow and compare it with conventional tillage, regarding soil physical properties and yield. The experiment was conducted in an Oxisol in the city of Jaguariuna, Brazil. The comparison was made between the soil physical properties: soil bulk density, porosity, macroporosity, microporosity and penetration resistance. At the end, a biometric evaluation of the crop was carried out in both areas. The soil properties showed few statistically significant variations, and the production showed no statistical difference. The rotary paraplow proved to be an applicable tool in the cultivation of sugarcane and has the advantage of being an invention adapted to Brazilian soils, bringing a new form of minimal tillage to areas of sugarcane with less tilling on the soil surface, in addition to reducing machine traffic. Full article

Path exits for transhumant livestock are a major problem in many Sub-Saharan African countries. These problems contribute to many community conflicts. Several solutions are currently being studied, including dialogues between stakeholders. In this paper, we propose a numerical approach to address the problem. Based on a commonly accepted model of agent movement, we propose a path simulator to estimate and quantify the risk of exiting the path. This enables quantitative estimation of the exit rates of transhumant animals as a function of the geometric properties of the routes. This model is tested on real transhumance routes in Chad to evaluate the risks of exits along these routes. These new data allow us to better understand the geometric properties on real routes and to evaluate them in terms of exit risk, giving new information to this complex problem. Although our approach does not deal with the whole complexity of this problem, it opens the door to field experimentation with geolocation sensors. Full article

Thyme (Thymus vulgaris) was dried using a tray dryer, recirculating tray dryer, and vacuum dryer at 35 °C, 40 °C, and 45 °C, respectively. The dried thyme after attaining 5% moisture content was subjected to a grinding process to obtain powder using a hammer mill for further analysis of physiochemical properties, bioactive compounds, and techno-functional properties. The ash content was 10.21%, fiber content was 13.57%, fat content was 1.69%, protein content was 5.61%, and carbohydrate content was 22.91% for the thyme sample dried at 35 °C via vacuum drying. Meanwhile, regarding the functional properties, the swelling power was 0.31%, dispersibility was 27.72%, emulsion capacity was 35.44%, foam capacity was 35.47%, and foam stability was 1.84% for the thyme sample dried at 40 °C in the vacuum dryer. The total chlorophyll content, ascorbic acid content, and bioactive compounds were retained best in the vacuum-dried sample at 40 °C. Bioactive compound retention for VDT among the selected three techniques at 35 °C was considerably better. The color values were found to be similar to those of freshly harvested thyme (hue, 93.39; chroma, 3.47) for the thyme sample dried at 40 °C in a vacuum dryer. Based on the analysis, it was found that vacuum drying at 40 °C gave better results, followed by the recirculating tray dryer at 40 °C and the tray dryer at 40 °C. The adequately dried thyme samples with the time–temperature combinations for different drying techniques used can be used further for product development and studies on their shelf life. Full article

Soil color serves as a critical indicator of its properties and conditions. It is shaped by a complex interplay of mineral and organic matter content, moisture levels, and other environmental variables. Additionally, human activities such as land-use changes and intensive agricultural practices can profoundly alter soil color. Soil color, driven by the presence of organic matter, plays a crucial role in understanding soil fertility. Its strong correlation with soil organic carbon content makes it a valuable parameter for assessing soil quality in agricultural practices. A variety of techniques have been developed to measure soil color, ranging from traditional Munsell color matching to modern color meters. Digital image colorimetry enables rapid on-site assessments of soil color, but environmental conditions such as soil water content and lighting conditions should be considered. Spectroscopic methods, particularly diffuse reflectance spectroscopy, pave the way for a more reliable and accurate composition analysis. Advances in remote sensing and computational methods are combined to explore the intricate relationships between soil color and environmental factors. Such an integrated approach not only enhances scalability but also leads to more insights and actionable strategies for environmental management and sustainable agriculture. Full article

This paper presents the validation of a digital image analysis method for the quantitative determination of physical quality parameters of apples by comparative analysis with a traditional measurement method. The method was used to determine the quality indicators of apples based on digital image analysis in accordance with standard requirements. Five popular apple varieties from Kazakhstan were selected for the study: Aport Alexander, Ainur, Sinap Almaty, Nursat and Kazakhskij Yubilejnyj. The parameters of the five apple varieties were measured both manually and digitally, which revealed close agreement between the obtained values. The analysis of the results of measuring the geometric parameters of the apples and the percentage of red color in the images was carried out. The maximum relative errors in determining the diameters (d, D) and height (h) were 2.99%, 3.03% and 4.12%, respectively. Regression models were developed to predict the weight and volume of apples. The best results in weight prediction were obtained for the Sinap Almatinsky variety using stepwise linear regression (R² = 0.96), and volume prediction showed the best results for the Nursat variety (R² = 0.92). This study lays the foundation for the development of automated systems for sorting apples by commercial varieties. Full article

This paper presents research on the impact of vibrations on the hand–arm and body system of agricultural tractor operators as ergonomic indicators in relation to certain operational parameters. The measurements were conducted on a LANDINI POWERFARM 100 tractor on agricultural production areas and access roads of the Agricultural and Veterinary School in Osijek. The measurements followed the ISO 5008:2015 standard, which describes the creation of test tracks: a smooth track of 100 m in length and a rough track of 35 m in length. Body vibration measurements were conducted according to the prescribed standards HRN ISO 2631-1: 1999/A1:2019 and HRN ISO 2631-4:2010. Hand–arm system vibration measurements were performed according to the prescribed standards HRN ISO 5349-1:2008 and HRN ISO 5349-2:2008/A1:2015. After the measured data were processed, a three-factor analysis of variance was performed, where some operational parameters were designated as A—agrotechnical surfaces (6 types), B—tractor speed (6 speeds), and C—tire air pressure (3 pressures), along with multiple regression analysis and the AHP (analytical hierarchical process). This research determined that none of the measured hand–arm system vibrations exceeded the warning (2.5 ms⁻²) or limit (5 ms⁻²) values of daily exposure. Furthermore, vibrations affecting the operator’s body in the x-axis at higher speeds and pressures C2 and C3, in the y-axis at higher speeds and pressures C1 and C2, and in the z-axis at the highest speed and pressures C1 and C2 were found to exceed the daily exposure warning value of 0.5 ms⁻². It was concluded that the operator’s health is at risk, and it is recommended that the seat’s air suspension system be inspected to prevent further complications in a timely manner. Full article

Early diagnosis of subclinical ketosis is fundamental in the production management of dairy cattle. Without evident clinical signs, this pathological condition causes important economic losses for the farmer and significant health repercussions for the cattle that could develop an altered immune function. Laboratory techniques, although accurate, are expensive, invasive, and cannot be used for real-time monitoring of the entire herd. On the contrary, the analysis of volatile organic compounds (VOCs) contained in the breath of dairy cattle affected by ketosis could represent a key biomarker of the ketogenic process. For this reason, we developed a sensory device, tested in the laboratory, to detect acetone concentrations ranging from 1 to 10 ppm (concentrations typically detected in the cow’s breath), and we look to verify the electronic nose’s potential as a non-invasive diagnostic tool for ketosis. Experimental results show the high sensitivity of the instrument in differentiating acetone solutions. Principal component analysis (PCA) showed a clear separation of samples in the score plot, while classification using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) achieved accuracy rates above 70% and 85%, respectively. These findings suggest the potential application of the electronic nose as a non-invasive diagnostic tool in veterinary diagnostic studies. In particular, its ability to detect and discriminate low acetone concentrations could help the farmer to improve the overall management of the herd, optimising monitoring strategies and ketosis diagnosis before the appearance of the clinical signs of the disease. Full article

The present work unveils the role of small farming plots (less than 5 ha) in the context of food security. It determines their contribution by estimating the spatial distribution (location), the crop types (diversity), the crop area extent (acreage), and the yield (production), factors that remain unclear, mainly because the official statistical offices rarely include them in surveys. The development introduces a novel RS-based approach that fulfills this gap. It provides stakeholders with the appropriate tools to accurately and timely acquire crop type map information and objectively quantify their crop production capabilities. Approaches based on the Land Parcel Identification System (LPIS) of the Integrated Administration and Control System (IACS) applied by many countries in Europe are proved useful in providing information on location, diversity, and acreage but not crop production per farm owner applying and eligible for receiving subsidies. The developed RS approach is implemented in twenty European NUTS-3 regions and one in Africa. Nevertheless, in this research, we focus on its development, testing, and evaluation in three pilot prefectures of Greece, producing the corresponding land cover maps. Notably, the unbiased crop area computation and the crop production estimates are performed only for the highly accurate key crop products (per crop type classification, FScore > 75%), considering that the key crop production estimations are obtained by combining the key crop areas with the field-level yields provided by the key informant surveys. The above choice ensures that the estimation of crop production will be derived only for the best-classified crops per reference region. The RS approach reduces the error propagation when estimating the area and production of the crop types that are classified with low or very low accuracy levels. These levels could reduce the strength of the overall conclusions about the main contributions of small farming plots. Potential changes occurring in the key crop cultivations of small farming plots are also estimated and mapped using the LPIS geodatabase. Under various environmental and territorial conditions, the results of the RS approach show good classification accuracies for several key crops per reference region. Their integration with the existing official statistical data and those derived from the LPIS geodatabase shows the consistency and significant contribution in estimating all the factors needed to determine the small farming plots. Finally, the applied innovative integrated approach can be expanded beyond the Greek case to cover other regions with various agricultural practices. Full article

This paper concerns the methods and algorithms developed for tracking changes in the internal characteristics of eggs during storage that emphasize rapid and simple classification and satisfactory regression accuracy, considering technological requirements. A thorough analysis was carried out for 53 characteristics of yolk, thick albumen, and thin albumen, followed by feature selection using the most informative features. Among others, three feature selection methods (FSRNCA, SFCPP, and RReliefF) gave comparably good results and allowed for the identification of the most important features. The results show that with features selected from data across three different manufacturers of hen eggs, more features gave positive cross-validation results and normal distribution checks in each storage stage. This infers that yolk characteristics are more stable and predictable than albumen characteristics. The study also identifies that the numbers and informativeness of selected features of yolk and albumen for hen eggs exceed those for quail eggs, including for cross-validation. Quail eggs give very varied results depending on the producer. Over 60% of the selected albumen features for manufacturers M1 and M2 showed strong performance in both normal distribution checks and cross-validation at different storage stages, while for producer M3, only 17% of features met these criteria. Among the yolk features selected for producers M2 and M3, more than half showed positive outcomes from normal distribution to cross-validation. For producer M1, only 4% of the yolk features passed these criteria at all storage stages. These results highlight the feature stability and predictability variation across different types of eggs and their manufacturers. Full article

Olive cultivation is still concentrated within the Mediterranean basin, although the last thirty years have seen an expansion into geographical areas outside it. Traditional olive groves, with large planting distances and centuries-old trees, still predominate. However, more and more space is being given over to modern plantations, which allow an ever-increasing degree of mechanisation, although some legal restrictions, often related to the monumental nature of the plantations, make the conversion of old plantations into new ones not always easy. The extreme case is super-intensive olive growing, where the very concept of olive growing has been rethought. In this context, harvesting is the most time-consuming and costly of the cultivation operations. Without it, or rather without a high degree of mechanisation, it is still not possible to produce high-quality oils. A leading role is always played by the trunk shakers, who are still the undisputed protagonists in this sector. This review looks at trunk shakers in olive groves, showing the latest models, and their strengths and weaknesses, based on the research carried out in recent decades. Full article