AgriEngineering, Volume 6, Issue 4 (December 2024) – 53 articles

Manual weed control is becoming increasingly costly, necessitating the development of alternative methods. This work investigates the feasibility of using laser technology for autonomous weed regulation. We developed a system utilizing a laser scanner to target and eliminate weeds, which was first tested using a pilot laser for accuracy and performance. Subsequently, the system was upgraded with a high-power fiber laser. Experimental results demonstrated a high weed destruction accuracy with real-time capabilities. The system achieved efficient weed control with minimal environmental impact, providing a potential alternative for sustainable agriculture. Full article

High-resolution images captured by Unmanned Aerial Vehicles (UAVs) play a vital role in precision agriculture, particularly in evaluating crop health and detecting weeds. However, the detailed pixel information in these images makes classification a time-consuming and resource-intensive process. Despite these challenges, UAV imagery is increasingly utilized for various agricultural classification tasks. This study introduces an automatic classification method designed to streamline the process, specifically targeting cassava plants, weeds, and soil classification. The approach combines K-means unsupervised classification with spectral trend-based labeling, significantly reducing the need for manual intervention. The method ensures reliable and accurate classification results by leveraging color indices derived from RGB data and applying mean-shift filtering parameters. Key findings reveal that the combination of the blue (B) channel, Visible Atmospherically Resistant Index (VARI), and color index (CI) with filtering parameters, including a spatial radius (sp) = 5 and a color radius (sr) = 10, effectively differentiates soil from vegetation. Notably, using the green (G) channel, excess red (ExR), and excess green (ExG) with filtering parameters (sp = 10, sr = 20) successfully distinguishes cassava from weeds. The classification maps generated by this method achieved high kappa coefficients of 0.96, with accuracy levels comparable to supervised methods like Random Forest classification. This technique offers significant reductions in processing time compared to traditional methods and does not require training data, making it adaptable to different cassava fields captured by various UAV-mounted optical sensors. Ultimately, the proposed classification process minimizes manual intervention by incorporating efficient pre-processing steps into the classification workflow, making it a valuable tool for precision agriculture. Full article

In tropical regions, maintaining crop residues in the soil is challenging. Silicon (Si) may increase the persistence of these residues in the soil, as it is a precursor to lignin, providing a gradual release of nutrients for subsequent crops. Therefore, the objective of this study was to evaluate the influence of different doses of calcium silicate (Ca₂SiO₄) (0, 1, 2, and 3 Mg ha⁻¹) and limestone (0, 1, 2, and 3 Mg ha⁻¹) on the lignin content, residue decomposition, and nutrient release of four cover crops—Pennisetum glaucum, Urochloa ruziziensis, Crotalaria spectabilis, and Cajanus cajan—at various decomposition stages following cover crop management (0, 30, 60, 90, and 120 days). The experiment was conducted in the field at the experimental area of the Faculty of Engineering at Ilha Solteira-UNESP, located in the municipality of Selvíria, state of Mato Grosso do Sul, on Ferralsol. The decomposition rate of the residues was assessed using the decomposition bag method, which was installed after cover crop management. The concentrations of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), Si, lignin, and cellulose were determined. Silicate application did not affect the accumulation of nutrients by cover crops and their release into the soil. There was no relationship between the remaining Si in the dry matter of plants and more persistent residues. The most persistent plants had higher final dry matter lignin content. Using pearl millet and pigeon peas resulted in more persistent residues in the soil. Full article

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction. Full article

The tomato (Solanum lycopersicum) is the second most produced vegetable globally, playing a significant role in national and international economies. This crop is highly sensitive to water deficit and thermal stress, which directly affect yield and fruit quality. Foliar application of calcium carbonate (CaCO₃) may be a possible strategy to minimize the effects of these abiotic stresses. This research aimed to determine: (a) the effects of different water replacement levels (WRLs) and photoprotector strategies (Ps) applied to the canopy on production and harvest quality of tomato fruit, (b) thermal responses—Crop Water Stress Index (CWSI) and soil temperature and (c) crop water productivity (WP_c). The research was conducted at the University of São Paulo (USP/ESALQ), Piracicaba, State of São Paulo, Brazil. The experimental design adopted was randomized blocks, with four blocks and nine treatments, totaling 36 plots. The treatments were arranged in a 3 × 3 factorial scheme, with three WRLs (70, 100 and 130% of the required irrigation depth) and three photoprotector strategies (without photoprotector, with photoprotector and with photoprotector + adjuvant). Biometric and thermal responses, productivity, harvest quality and WP_c were determined. The highest plant height and stalk diameter values were found in the treatment with photoprotector and adjuvant, with an average of 0.98 m and 0.0130 m, respectively. For the variables soil temperature, CWSI and tomato productivity, no significant differences were observed. The general average productivity obtained was 77.9 Mg ha⁻¹. The highest WP_c values were found in the WRL 70 treatments, with an average of 23.6 kg m⁻³. No significant differences were observed for pulp firmness. The highest average value of soluble solids was observed in the treatments with photoprotector (4.8 °Brix) and the highest average value of titratable acidity was observed in the WRL 130 treatments (0.36%). Therefore, deficit irrigation resulted in water savings without compromising tomato productivity and the application of photoprotector and adjuvant increased tomato quality. Full article

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management. Full article

In addition to being an important instrument in the search for increasingly greater productivity, agricultural production with adequate use of irrigation systems significantly minimizes the impact on water resources. To meet high productivity and yield, as well as industrial quality, a series of studies on sugarcane cultivation are necessary. Despite being able to adapt to drought, sugarcane is still a crop highly dependent on irrigation to guarantee the best quality standards. Our study aimed to analyze the agronomic performance and technological attributes of two sugarcane cultivars, evaluating the vegetative and productive pattern, as well as the industrial quality of the cultivars RB92579 and SP80–1816, which were cultivated under split-irrigation management in the Sugarcane Research Unit of IF Goiano—Campus Ceres, located in the state of Goiás in the Central-West region of Brazil. A self-propelled sprinkler irrigation system (IrrigaBrasil) was used, duly equipped with Twin 120 Komet sprinklers (Fremon, USA). The cultivars were propagated vegetatively and planted in 0.25 m deep furrows with 1.5 m between rows. The experiment was conducted in a completely randomized design (CRD), with a bifactorial split-plot scheme (5 × 2), with four replications, where the experimental plots were subjected to one of the following five split-irrigation management systems: 00 mm + 00 mm; 20 mm + 40 mm; 30 mm + 30 mm; 40 mm + 20 mm; or 60 mm + 00 mm. At 60 and 150 days after planting (DAP), the following respective irrigation management systems were applied: 00 mm + 00 mm and 20 mm + 40 mm. Biometric and technological attributes, such as plant height (PH) and stem diameter (SD), were evaluated in this case at 30-day intervals, starting at 180 DAP and ending at 420 DAP. Measurements of soluble solids content (°Brix), apparent sucrose content (POL), fiber content (Fiber), juice purity (PZA), broth POL (BP), reducing sugars (RS), and total recoverable sugars (TRS) were made by sampling stems at harvest at 420 DAP. RB92579 showed total recoverable sugar contents 11.89% and 8.86% higher than those recorded for SP80–1816 under split-irrigation with 40 mm + 20 mm and 60 mm + 00 mm, respectively. Shoot productivity of RB92579 reached 187.15 t ha⁻¹ under split-irrigation with 60 mm + 00 mm, which was 42.16% higher than the shoot productivity observed for SP80–1816. Both cultivars showed higher qualitative and quantitative indices in treatments that applied higher volumes of water in the initial phase of the culture, coinciding with the dry season. Sugarcane cultivar RB92579 showed a better adaptation to the prevailing conditions in the study than the SP80–1816 cultivar. Full article

Carbon dioxide flux emissions (CFE) from agricultural areas exhibit spatial and temporal variability, and the best time of collar fixation to the soil prior to the collection of CO₂ flux, or even its existence as a factor, is unclear. The objective of this study was to evaluate the effect of the fixation time of collars that support the soil-gas flux chamber based on the influence of CFE on different pasture management practices: control (traditional pasture management practice) (CON), chisel (CHI), fertilized (FER), burned (BUR), integrated crop-livestock (iCL), and plowing and harrowing (PH). A field study was conducted on the clayey soil of Udults. The evaluations were performed monthly by fixing the PVC collars 30 d and 30 min prior to each CFE measurement. Although a linear trend in CFE was observed within each pasture management practice between the two collar-fixation times, collar fixation performed 30 min prior led to an overestimation of CFE by approximately 32.7% compared with 30 d of collar fixation. Thus, CFE were higher (p ≤ 0.10) in the MC, when compared to the FC, when the CON, BUR, and iCL managements were evaluated. Overall, fixing the collar 30 d prior to field data collection can improve the quality of the data, making the results more representative of actual field conditions. Full article

Citrus yield estimation using deep learning and unmanned aerial vehicles (UAVs) is an effective method that can potentially achieve high accuracy and labor savings. However, many citrus varieties with different fruit shapes and colors require varietal-specific fruit detection models, making it challenging to acquire a substantial number of images for each variety. Understanding the performance of models on constrained or biased image datasets is crucial for determining methods for improving model performance. In this study, we evaluated the accuracy of the You Only Look Once (YOLO) v8m, YOLOv9c, and YOLOv5mu models using constrained or biased image datasets to obtain fundamental knowledge for estimating the yield from UAV images of yellow maturing citrus (Citrus junos) trees. Our results demonstrate that the YOLOv5mu model performed better than the others based on the constrained 25-image datasets, achieving a higher average precision at an intersection over union of 0.50 (AP@50) (85.1%) than the YOLOv8m (80.3%) and YOLOv9c (81.6%) models in the training dataset. On the other hand, it was revealed that the performance improvement due to data augmentation was high for the YOLOv8m and YOLOv9c models. Moreover, the impact of the bias in the training dataset, such as the light condition and the coloring of the fruit, on the performance of the fruit detection model is demonstrated. These findings provide critical insights for selecting models based on the quantity and quality of the image data collected under actual field conditions. Full article

A protocol for detecting the potential occurrence of spontaneous combustion (SC) in stored cottonseeds and peanuts using a micro-incubator is described. The protocol indicates how to quantify CO₂ production rates and final CO₂ levels in wet versus dry cottonseed and peanut samples, which can provide crucial data for the early detection of SC risk in storage facilities. The experimental design utilizes a micro-incubator to simulate conditions found in large bulk crop storage. Parameters monitored include CO₂ concentration, temperature, and relative humidity. The protocol includes preparation methods, experimental procedures for both control (dry) and wet seed tests, and test termination criteria that allow for safe experimentation of likely pathogenic fungi. The protocol has three replicates for wet and dry conditions. The protocol is intended to facilitate future experimental studies and ultimately contribute to the development of a consistently reliable early warning fire detection system for SC in cottonseed and peanut warehouse facilities. A consistently reliable fire detection system would address a critical need in the cotton and peanut industry for improved fire risk management and insurability of storage facilities. Full article

Soil loss compromises ecosystem services essential for sustainable development, necessitating effective strategies to identify priority areas for conservation practices aimed at reducing soil erosion. Current methods often rely on literature-based classification, which can be subjective. This study explores the use of artificial intelligence techniques to enhance the objectivity and efficiency of qualitative classifications for soil erosion risk. Accordingly, the aims were to apply Machine Learning methods, specifically cluster analysis, to categorize soil erosion risk in the Peixe Angical Basin, in addition to using a discriminant analysis to propose a discriminant classifier vectors for current and future predictions of soil loss risks. Our database consisted of pixel-based data on the R, K, LS, and C factors. These input data were linked to soil losses (output data), which had been classified based on findings from studies conducted in a different basin. Following this, machine learning techniques were applied to analyze the data. The cluster analysis identified seven distinct erosion risk groups: slight, slight to moderate, moderate, moderate to severe, severe, very severe, and extremely severe. Additionally, discriminant analysis facilitated the development of seven predictive models for current and future soil erosion risk, streamlining the need of new soil erosion modeling and enhancing decision-making processes. We anticipate that this methodology can be applied to other basins, providing a more robust framework for assessing soil erosion risk without relying on arbitrary qualitative classification. Full article

Various cultivars of citrus fruits have unique constructions, such as thick outer skin. These constructions generate gas diffusion resistance between the atmosphere and the fruit, which can limit the gas exchange of O₂ and CO₂.This has not been sufficiently investigated. This study on seven cultivars of citrus fruit firstly aimed to investigate gas diffusion resistance utilizing the ethane efflux method; secondly, this study aimed to investigate the internal gas concentration of O₂ and CO₂. As a result, a cultivar of citrus fruit with slimmer outer skin thickness had lower resistance. For the internal gas, a high CO₂ concentration in comparison with the atmosphere was observed even in the fruits with the minimum resistance, and no considerable difference was observed among all cultivars, regardless of the gas diffusion resistance value. However, when the fruits were stored at 25 °C for 2 weeks, CO₂ gas concentration tended to increase and O₂ gas concentration tended to decrease, with an increase in the resistance value. Therefore, when the respiration of citrus fruits is activated at ambient temperature, the self-control system of internal gas concentration can be driven to suppress the respiration which was induced by gas diffusion resistance generated from their construction. Full article

In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress indicators, were derived by analyzing the EEG data collected. The EEG analysis revealed that agricultural work stress manifested when participants engaged in agricultural tasks following a period of rest. Additionally, the right prefrontal cortex was identified where the values of SEF95% and RGP increased concurrently with the rise in noise (61.42–88.39 dBA) and vibration (0.332–1.598 m/s²). This study’s results are expected to be utilized as foundational data to determine the agricultural work stress felt by farmers during work through EEG analysis in response to changes in noise and vibration. Full article

Tomato fungal diseases can cause significant economic losses to farmers. Advanced disease detection methods based on symptom recognition in images face challenges when identifying fungal diseases in tomatoes, especially with limited training images. This study utilized novel techniques designed for limited data scenarios, such as one-shot and few-shot learning, to identify three tomato fungal diseases, i.e., Alternaria solani, Alternaria alternata, and Botrytis cinerea. Automated feature extraction was performed using the ResNet-12 deep model, and a cosine similarity approach was employed during shot learning. The accuracy of diagnosing the three diseases and healthy leaves using the 4-way 1-shot learning method was 91.64, 92.37, 92.93, and 100%. For the 4-way 3-shot learning method, the accuracy improved to 92.75, 95.07, 96.63, and 100%, respectively. These results demonstrate that the proposed method effectively reduces the dependence on experts labeling images, working well with small datasets and enhancing plant disease identification. Full article

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows. Full article

In Japan, the aging and decreasing number of agricultural workers is a significant problem. For wine grape harvesting, especially for large farming areas, there is physical strain to farmers. In order to solve this problem, this study focuses on developing an automated harvesting robot for wine grapes. The harvesting robot needs high dust, water, and mud resistance because grapevines are grown in hard conditions. Therefore, a three-axis linear robot was developed using a rack and pinion mechanism in this study, which can be used in outdoor conditions with low cost. Three brushless DC motors were utilized to drive the three-axis linear robot. The motors were controlled using a control area network (CAN) bus to simplify the hardware system. The accuracy of the robot positioning was evaluated at the automated harvesting condition. The experiment results show that the accuracy is approximately 5 mm, 9 mm, and 9 mm in the x-axis (horizontal), y-axis (vertical), and z-axis (depth), respectively. In order to improve the accuracy, we constructed an error model of the robot and conducted a calibration of the robot. The accuracy was improved to around 2 mm of all three axes after calibration. The experimental results show that the accuracy of the robot is high enough for automated harvesting of the wine grapes. Full article

Aiming to address the problems of poor separation of peanuts and soil and severe damage of pods during peanut harvesting in saline soil, a peanut digging and harvesting machine was designed using extrusion shaking vibration and roller extrusion. Theoretical calculations determined the structural parameters of critical components. The law of motion of the seedling soil assemblage at the stage of separation and transportation was derived by analyzing the kinematic properties. The soil extrusion vibration crushing dispersion and sieving process was analyzed, and the factors affecting soil crushing and separation were determined by establishing the extrusion collision model. One-way and orthogonal tests used soil content, breakage, and loss rates as test indicators. The orthogonal test showed that the working parameters were as follows: working speed was 0.889 m/s, the inclination angle was 21.5°, the working line speed of the sieve surface was 2.00 m/s and the roller gap of the roller squeezing device was 37 mm, the peanut harvesting rate of soil content was 1.36%, the breakage rate was 0.78%, and the loss rate was 1.15%. The paper references developing a peanut harvester for clay-heavy soil with soil separation performance improvement. Full article

Soil monitoring is essential for promoting sustainability in agriculture, as it helps prevent degradation and optimize the use of natural resources. The introduction of innovative technologies, such as low-cost sensors and intelligent systems, enables the acquisition of real-time data on soil health, increasing productivity and product quality while reducing waste and environmental impact. This study examines various agricultural monitoring technologies, focusing on soil moisture sensors and nutrient detection, along with examples of IoT-based systems. The main characteristics of these technologies are analyzed, providing an overview of their effectiveness and the key differences among various tools for optimizing agricultural management. The aim of the review is to support an informed choice of the most appropriate sensors and technologies, thus contributing to the promotion of sustainable agricultural practices. Full article

Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha⁻¹ of nitrogen, in addition to the control (0 kg ha⁻¹), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 < R² < 0.87; 1.00 < RMSE < 1.50 kg ha⁻¹) in comparison with the individual prediction by band (0.69 < R² < 0.85; 1.00 < RMSE < 1.77 kg ha⁻¹) and by VI (0.55 < R² < 0.68; 1.00 < RMSE < 1.78 kg ha⁻¹). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC. Full article

Sentinel-2 (S2) is widely considered a reliable satellite constellation for monitoring several crops, such as grapevine (Vitis vinifera L.). A large dataset of Italian vineyards randomly chosen was monitored with S2 from 2017 to 2022. Two vegetation indices (VIs) and their statistics were calculated from each vineyard. In addition, structural features and topographic information were assessed using Google Earth and national databases. The research study aims to identify the most relevant drivers of spatial variability by assessing the VIs among the whole dataset and the within-vineyard variability. The latitude and the vintage showed the most relevant effect on spatial variability, depicting the effect of daylight hours, climate conditions and weather events. However, the vintage did not affect the patterns of the within-field variability. Regarding grapevine management, training systems and the rows’ orientation were relevant boosters of variability. While the vineyards planted with north–south-oriented rows reached the highest VIs values, the east–west-oriented ones showed the highest variability. Finally, an interaction effect was detected between hill or plain plantation and the terrain slope on both the average and variability of the VIs. The conclusions from the present study suggest the relevance of clustering vineyards under remote supervision according to the structural features to reduce data variability. Further studies should investigate other structural features or managerial properties. Full article

The rice variety Bomba is grown in Calasparra—a rice of origin in southeast Spain—resulting in a product with excellent cooking quality, although its profitability has declined in recent years due to low grain yields and susceptibility to rice blast disease (Pyricularia oryzae Cavara). An innovation project to test the efficacy of mechanized transplanting against traditional direct seed sowing was conducted in 2022 and 2023 at four locations for the first time. A lower plant density (67–82 plants m⁻²) and shorter plants with higher leaf nitrogen content were observed in transplanted plots compared with seed sowing (130–137 plants m⁻²) in the first year. The optimal climatic conditions for P. oryzae symptom appearance were determined as temperatures of 25–29 °C and a 50–77% relative humidity. The most-affected sowing plots presented 3–20% leaf area damage and a reduction in yield to values of 1.5 t ha⁻¹ in the first year and 2.12 t ha⁻¹ in the second year. In transplanted plots, there was generally less humidity at the plant level and therefore, disease incidence was low in both seasons. Grain yields did not significantly differ among the treatments studied; however, there were differences in the yield components of panicle density and the number of grains for panicles. Principal component analysis revealed two principal components that explained 81% of the variability. Variables related to yield contributed positively to the first component, while plant biomass variables contributed to the second component. Plant density, tiller density, and panicle density were found to be positively correlated (r > 0.81 ***). Overall, transplanting (frame of 30 × 15–18 cm²) resulted in uniform crop growth with less rice blast disease, as well as higher grain yields (2.92–3.89 t ha⁻¹), in comparison with the average for the whole D.O. Calasparra (2.3–2.5 t ha⁻¹) in both seasons and a good percentage of whole grains at milling. This is novel knowledge which can be considered useful for farmers operating in the region. Full article

Insects are a valuable and renewable source of feed and food protein and fat. They have an amino acid composition similar to that of fishmeal and meat, and can serve as a worthy replacement for them. The aim of this study was to substantiate the technological parameters of the process of obtaining fat from the Hermetia illucens larvae by a mechanical method on a screw press. A laboratory screw press was used for this research. Before squeezing out the fat, the dried larvae were moistened, crushed and heated in a microwave oven to a temperature of 60 °C. The fat from the larvae was squeezed out in a screw press at different larval moisture levels, screw speeds and cake outlets. The results of this study made it possible to obtain optimal technological parameters for obtaining fat on a screw press: a screw rotation speed of no more than 20 ± 5 rpm; a diameter of the hole for the cake outlet of no more than 7–10 mm; a mass fraction of moisture in the pressed material of 8 ± 2%. The obtained fat fraction was tested for one of the main indicators of fat quality—acid number. It was found that the variable factors do not have a significant effect on the acid number of fat, changing it within the normal range of 10 mg KOH per 1 g of fat, which makes it possible to obtain a good quality product. Full article

Currently, Brazilian coffee farming seeks the rational use of resources through sustainable practices. As a result, the development of machinery with more efficient input application systems and the adoption of precision agriculture techniques have been yielding excellent results. This study was divided into two stages, with the first involving the adaptation of a solid fertilizer application machine with fixed doses, allowing dose variation using an electronic controller. The second stage consisted of conducting trials and their applications under operational conditions. The results confirmed that the developed system remained stable in terms of variable-rate fertilizer distribution for coffee cultivation. The machine’s lateral fertilizer distribution range met the demands of coffee farming satisfactorily. In field conditions, the developed system exhibited an average error of −2.9%, compared to the programmed doses, validating the accuracy of the machine and its suitability for use in coffee plantations. Full article

This review article provides a comprehensive analysis of the technical advancements and research trends in solar drying technologies for agricultural products. The study encompasses various innovations in energy storage systems, including phase change materials (PCMs) and the use of computational fluid dynamics (CFD) for optimizing the drying process. Through a bibliometric analysis of 126 scientific papers published between 1984 and 2024, five major research clusters were identified: energy generation, heat transfer, thermal storage, simulation modeling, and the integration of hybrid systems. The results demonstrate a marked increase in scientific output over the past decade, emphasizing a growing interest in the sustainable use of solar energy for drying applications. Key findings highlight that while PCM-based storage solutions significantly enhance the thermal stability of dryers, the high implementation costs and technical complexities limit their adoption, especially in small-scale operations. Similarly, CFD models have proven effective in optimizing air and temperature distribution within dryers; however, their performance is hindered by real-world fluctuations in solar radiation and humidity levels. To address these limitations, future research should focus on the development of cost-effective PCM materials and the improvement of CFD models for dynamic environmental conditions. The review concludes by emphasizing the importance of interdisciplinary collaboration in the design and application of these technologies, recommending the inclusion of real-world case studies to better illustrate the practical implications and economic benefits of solar drying technologies for agricultural production. Full article

Climate change has intensified droughts, severely impacting crops like oats and highlighting the need for effective adaptation strategies. In this context, the implementation of IoT-based climate control systems in greenhouses emerges as a promising solution for optimizing microclimates. These systems allow for the precise monitoring and adjustment of critical variables such as temperature, humidity, vapor pressure deficit (VPD), and photosynthetically active radiation (PAR), ensuring optimal conditions for crop growth. During the experiment, the average daytime temperature was 22.6 °C and the nighttime temperature was 15.7 °C. The average relative humidity was 60%, with a VPD of 0.46 kPa during the day and 1.26 kPa at night, while the PAR reached an average of 267 μmol m⁻² s⁻¹. Additionally, the use of high-throughput gravimetric phenotyping platforms enabled precise data collection on the plant–soil–atmosphere relationship, providing exhaustive control over water balance and irrigation. This facilitated the evaluation of the physiological response of plants to abiotic stress. Inoculation with microbial consortia (PGPB) was used as a tool to mitigate water stress. In this 69-day study, irrigation was suspended in specific treatments to simulate drought, and it was observed that inoculated plants maintained chlorophyll b and carotenoid levels akin to those of irrigated plants, indicating greater tolerance to water deficit. These plants also exhibited greater efficiency in dissipating light energy and rapid recovery after rehydration. The results underscore the potential of combining IoT monitoring technologies, advanced phenotyping platforms, and microbial consortia to enhance crop resilience to climate change. Full article

The Purdue hog cooling pad has previously been demonstrated to mitigate heat stress in lactating sows by conductively transferring heat from a sow to cool water running through an integral heat exchanger. Coolant effectiveness, which describes how much heat is removed per volume of water flushed through the cooling pad, is used to compare the operation under varying conditions. Past studies have indicated that the intermittent flow of cooling water achieves a greater coolant effectiveness than continuous flow operational schemes. An electronic control system was implemented with the current cooling pad design to allow for the automated control of a solenoid valve to create the intermittent flow conditions. All testing was performed using 18 ± 1 °C inlet water. Potential control schemes were categorized into two groups, temporal and temperature threshold. The temporal schemes opened the solenoid for 30 s, enough time to flush the entire contents of the cooling coils, before closing for 3, 6, or 9 min. The temperature threshold control schemes utilized feedback from thermal probes embedded beneath the surface of the cooling pad to open the solenoid for 30 s, when a maximum surface temperature was detected. Trigger temperatures of 28.0, 29.5, or 31.0 °C were used. The temperature threshold control schemes achieved greater heat transfer rates (348, 383, 268 W) compared to the temporal control schemes (324, 128, 84 W). The cooling effectiveness for all control schemes ranged from 46.6 to 64.7 kJ/L. The tested intermittent flow control schemes in this study achieved greater cooling effectiveness than continuous flow systems from previous studies (time: 51 kJ/L; temperature: 61 kJ/L; steady: 5.8 kJ/L), although the temporal control schemes exhibited lower heat transfer rates (time: 180 W; temperature: 330 W; steady: 305 W). Full article

Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption. Full article

In this study, we investigated the use of the pre-sowing electrophysical stimulation of seeds, particularly focusing on optimizing technological regimes for enhancing seed quality. The aim of this study was to improve sunflower seed germination utilizing laser optical radiation. The methods explored involved the pre-sowing stimulation of oilseeds and analyzing the key mechanisms affecting germination. Through our experimental research, we sought to identify the most effective laser irradiation parameters, ensuring the maximum seed quality improvement with minimal energy use. Using seeds of the first reproduction, we employed artificial aging to simulate a reduced seed quality and determined optimal irradiation regimes. Standard methods were followed to assess seed quality before and after irradiation, with 6–7 days of further exposure. Seed germination was carried out under controlled light and temperature conditions using the “on paper” method with paper napkins. A full factorial experiment was performed and key parameters for laser irradiation were determined, confirming that the pre-sowing laser pulse treatment significantly improved seed quality. In this research, we developed a biotechnical system for processing seeds and propose a method to adjust irradiation parameters based on the initial seed quality. The system effectively enhanced germination and crop yield, offering a reliable solution for improving sunflower seed productivity through laser treatment. Full article

The growing global demand for healthy, gluten-free snacks has driven the food industry to explore innovative products that fit consumer preferences. This study focused on developing a gluten-free, energy-dense, and crunchy snack called Quispiño, made from quinoa (Chenopodium quinoa Willd.), an ancient crop native to the Andes and particularly significant in Puno, Peru. Natural and desaponified quinoa samples were compared, revealing decreased carbohydrate content (69.75 g to 64.02 g per 100 g) and protein content (13.27 g to 12.90 g per 100 g) after desaponification. Moisture remained around 11.5%, while fiber content significantly decreased in the desaponified quinoa (from 4.39 g to 2.76 g per 100 g). The extrusion process influenced the color of the extrudates, reducing the L* value (from 75.28 to a range of 63.70–69.12), indicating darkening due to the Maillard reaction. Moisture in the extrudates ranged from 3.08% to 6.12%, while firmness varied between 7.25 N and 25.86 N, significantly influencing extrusion temperature. The water solubility index (WSI) ranged from 0.17% to 71.61%, with high values attributed to starch dextrinization during extrusion. The water absorption index (WAI) showed a significant increase, highlighting the physical changes induced by extrusion. The sectional expansion index (SEI) also varied considerably, ranging from 7.33 to 13.08, reflecting the impact of the extrusion process on the final product structure. The optimal sample was identified and subjected to an acceptability test with an untrained panel of 45 evaluators who assessed flavor, color, odor, appearance, and texture. The best-performing treatment was further analyzed for proximate composition, calcium, and iron content to compare with the raw material. The results demonstrate the potential of quinoa as a key ingredient in developing new, expanded, gluten-free snacks that meet the growing demand for nutritious and appealing food products in the global market. Full article

Soilless culture systems, which promote plant growth and enable the precise control of the root-zone environment, have yet to be fully established for sweet potatoes. In this study, we developed a soilless culture system and examined the effects of soil covering and light exposure on the storage roots of sweet potatoes. Sweet potato seedlings with induced storage roots were transplanted into five systems: a previously developed pot-based hydroponics system (Pot), an improved version with storage roots enclosed in a plastic box and covered with a soil sheet (SS), the SS system without the soil sheet (SD), the SD system with light exposure to storage roots after 54 days (SL), and a deep flow technique (DFT) hydroponics system. Our study enabled the time-course observation of storage root enlargement in the SS, SD, and SL systems. In the SL system, light exposure suppressed the storage root enlargement and reduced epidermal redness. No storage root enlargement was observed in the DFT system, even at 151 days after transplantation. Light exposure in the SL system increased the chlorophyll and total phenolic contents in the cortex beneath the epidermis, while the starch content was the lowest in this system. These findings indicate that the developed system can induce normal storage root enlargement without soil. Additionally, the observed changes in growth and composition due to light exposure suggest that this system is effective for controlling the root-zone environment of sweet potatoes. Full article