Smart Farming Technologies for Sustainable Agriculture: A Case Study of a Mediterranean Aromatic Farm

Abstract

Consumer interest in medicinal and aromatic herbs is on the rise, with buyers increasingly concerned about the microbiological quality of nutraceutical and aromatic plants. The use of Unmanned Aerial Vehicles (UAVs) and sensor technology allows for high-resolution crop monitoring, particularly in the production of rosemary and sage in Grotte (Italy), Agrigento District. The aim of this study is to evaluate the efficacy of UAV-based time series remote sensing data and multimodal data fusion using RGB and multispectral sensors in rosemary and sage harvesting time individuation and the microbiological quality of these nutraceutical and aromatic plants before and after an innovative and sustainable drying process. The multispectral data were acquired with a DJI multispectral camera mounted on a Phantom 4 UAV. The use of drones in the aromatic plant crops can lead to improved efficiency, productivity, and profitability for farmers and businesses. Italian producers follow strict hygiene regulations to reduce bacterial contamination, particularly during the crucial drying process. A rapid drying method at low temperature using a dryer powered by a photovoltaic renewable energy source (RES) helps preserve the quality of the plants. Real-time monitoring of the drying process is enabled through a system based on wireless sensor networks (WSN), providing valuable data on moisture content, drying rates, and microbial stability. Overall, the innovative use of drones, sensor technology, and renewable energy sources in the production of aromatic herbs like rosemary and sage holds great potential for enhancing crop quality, shelf life, and overall sustainability in the chain food industry.

1. Introduction

The agrifood sector plays a crucial role in meeting global food demands and supporting rural livelihoods, but it faces growing challenges due to climate change. Extreme weather and shifting seasons negatively impact agricultural production, especially for small-scale farmers who lack resources and infrastructure. To ensure the sector’s long-term viability, climate-smart strategies are essential. These strategies can help improve resilience, reduce production risks, and enhance incomes through sustainable practices and innovations such as precision agriculture, which fosters economic prosperity [1,2,3,4].

The global population is expected to surpass nine billion by 2050, highlighting the urgent need for increased food production. To address this, farmers are turning to technologies that optimize operations, but they also face challenges like climate variability. Smart farming (SF) has emerged as a promising solution by integrating information and communication technologies to improve agricultural productivity. SF enhances operational efficiency through spatial analysis of production data. Effective monitoring of environmental variables is essential for informed decision-making regarding agricultural practices like irrigation and harvest timing [5,6].

Several frameworks, such as Internet of Things (IoT)-enabled monitoring and cloud-based services, aim to improve farming practices, although more research is needed to guide the selection of IoT technologies for crop management [7,8,9].

Agriculture 4.0, the fourth agricultural revolution, uses digital technologies to create a more efficient and environmentally sustainable agricultural sector. Innovations like Big Data, AI, robotics, and IoT are central to transforming farming operations [10,11]. Smart farming and precision agriculture have automated farming processes, improving both the quantity and quality of crop yields to ensure food sustainability [12]. Precision agriculture (PA) focuses on optimizing resource use for maximum yield while minimizing environmental impact, with a strong emphasis on sustainability [13,14].

Smart farming extends precision agriculture by incorporating new technologies such as cloud computing, GIS, and remote sensing to manage crops more efficiently [15]. The integration of AI, drones, and IoT sensors enables autonomous farm management, further boosting crop yield. Precision agriculture, which includes technologies like GNSS for precise location data, enables farmers to make informed decisions for site-specific crop management [16]. The digitization of agriculture began in the 1990s with John Deere’s innovations in precision planting and fertilizer application, with the goal of increasing production while supporting sustainable practices.

Hyperspectral and multispectral sensors are essential in precision farming, providing valuable data for assessing crop health and optimizing farming practices. Unmanned Aerial Vehicles (UAVs) equipped with these sensors offer a cost-effective and flexible solution for monitoring crops and collecting high-resolution data on vegetation indices (VIs) that are crucial for understanding crop status [17,18]. These indices, including NDVI, MCARI, and TCARI, help estimate chlorophyll content, which is an indicator of photosynthetic activity and plant health [19,20,21,22,23].

Currently, consumer interest in medicinal and aromatic plants (MAPs) is growing, highlighting the need for effective monitoring and management to ensure quality and sustainability [24,25,26,27].

The use of UAVs in the cultivation of aromatic plants, like rosemary and sage, supports monitoring plant health, detecting diseases, and optimizing irrigation and fertilization. These data aid farmers in making decisions about harvest timing and improving crop yield [16]. In regions like Sicily, where traditional practices dominate, smart farming and precision agriculture can significantly enhance farming efficiency and crop yield [9,28,29,30,31,32].

As a part of advancing sustainable farming practices, IoT technology is applied to enhance agricultural efficiency. A recent prototype solar drying system, powered by photovoltaic energy in Grotte, Italy, demonstrates how IoT can optimize environmental factors like temperature and humidity, improving drying processes for MAPs [33,34].

This prototype AI-powered medicinal and aromatic crops smart solar fryer represents a significant step forward in agricultural technology. It improves the sustainability, efficiency, and quality of drying processes, benefiting farmers and producers who rely on MAPs for both their medicinal and aromatic properties. By combining solar energy with intelligent, data-driven systems, this approach ensures higher-quality products and a reduced environmental footprint [35,36].

This study aims to evaluate the optimal harvest time of two MAPs using multispectral sensors on UAVs (Figure 1). It focuses on assessing crop variability through vegetation indices (VIs), which can guide spatially tailored crop management practices through a Decision Support System (DSS).

Solar dryers offer significant economic and environmental benefits, particularly in reducing drying operation costs. For instance, farmers can save up to USD 757.31 annually and increase profits by USD 15,683 [37].

In Egypt, the use of solar dryers for aromatic crops like lemongrass and oregano can save between USD 102.5 and USD 1235.44 per year, depending on the rate of utilization [38]. The payback period for solar dryers varies, with a direct solar dryer for medicinal plants having a payback period as short as 6 months, while an indirect dryer may take up to 14 months [39]. Furthermore, hybrid solar dryer models, especially those integrated with photovoltaic cells, offer enhanced energy efficiency, achieving up to 70% efficiency [40]. Environmentally, solar dryers help significantly reduce CO₂ emissions; for example, drying grapes in Egypt can reduce emissions by up to 407,023.8 kg annually [38], and overall, solar dryers can mitigate up to 430,714.76 tons of CO₂ emissions each year [37]. Solar dryers also contribute positively to sustainability indices and can earn carbon credits, providing additional financial benefits [41]. However, the initial investment required for solar dryers can be substantial, which may deter some farmers. Hybrid and mixed-mode dryers, although requiring 15–25% more initial investment, reduce drying time by 30–40% [42]. Adoption rates are also hindered by financial and operational barriers, but multi-seasonal use and financing schemes could alleviate these challenges [43]. In conclusion, solar dryers present both economic and environmental advantages, including significant cost savings, increased revenue, and reductions in CO₂ emissions. Despite high initial costs, long-term benefits and the potential for carbon credits make them a viable and sustainable option for agriculture, with financial incentives and multi-seasonal usage further encouraging their adoption.

The use of AI and solar power not only reduces operational costs but also improves crop quality, sustainability, and overall profitability [44,45].

Drying, a critical preservation method, helps reduce microbial growth and maintain the quality of herbs, including flavor and nutrients. Solar drying, in particular, is an effective technique for preserving the integrity of medicinal plants, aiding in the quality control of nutraceutical and aromatic products [44,45].

The comparison between natural drying in open air and smart solar dryers (SSDs) highlights several key differences in drying efficiency, product quality, and operational reliability. Natural drying in open air relies on direct sunlight and ambient air to remove moisture from food products. It is a simple, low-cost method with minimal infrastructure requirements. However, it is highly dependent on weather conditions, making it unreliable during cloudy or rainy periods. Additionally, there is a risk of contamination from dust, insects, and animals, which can compromise the hygiene and quality of the product. The drying process is also inconsistent, leading to uneven moisture removal and potential nutrient loss, requiring large areas for spreading out the products. In contrast, the SSD uses controlled solar energy, often integrated with photovoltaic systems, thermal energy storage, and forced convection, to enhance drying efficiency. This system offers higher drying rates, more effective moisture removal, and improved product quality with less shrinkage and better color retention. It also reduces the risk of contamination by enclosing the products, making it more hygienic than open-air drying. SSDs can operate in various weather conditions, including cloudy weather or at night, thanks to thermal energy storage systems. However, they come with a higher initial cost and require technical expertise for operation and maintenance. Despite these drawbacks, SSDs are economically viable, with shorter payback periods and demonstrated economic benefits. Overall, while natural drying remains a cost-effective and simple option, SSDs offer superior efficiency, product quality, and hygiene, making them a preferable choice for modern agricultural practices [46,47,48,49,50,51].

The quality of nutraceutical and aromatic plants from a microbiological standpoint is crucial for customers. Italian producers follow strict hygiene regulations to reduce the bacterial presence in these plants, especially focusing on the drying process between harvest and packaging [52,53,54]. Therefore, the study explores optimizing the drying process in Grotte using a photovoltaic-powered dryer, aiming to enhance the quality and sustainability of aromatic species like rosemary and sage [33,34,35,36], as shown in this diagram (Figure 1).

Hence, the specific objectives of this study were to (i) assess the suitability for a sustainable cultivation of medicinal and aromatic plants (MAPs), i.e., rosemary and sage in Sicily; (ii) explore the use of UAVs (drones) for spectral index analysis; (iii) use sensors to optimize the drying process of aromatic herbs using a smart solar dryer (SSD); and (iv) evaluate the microbiological characteristics of sage and rosemary before and after drying to validate the quality of the process (Figure 1).

2. Materials and Methods

2.1. Description of the Site

The study site is located in the inland area of southern Sicily, specifically in Grotte (37.381770° N, 13.673952° E) in the province of Agrigento, near the Morreale company. The land is situated at an altitude of about 400 m above sea level. According to Köppen’s classification, the area is characterized by a hot temperate climate (Csa). The moisture regime of the soils is xeric, bordering on aridic, and the thermal temperature regime is also noted.

The Morreale farm’s primarily specializes in growing aromatic and nutraceutical plants, including Oregano (Origanum vulgare L.), Rosemary (Salvia rosmarinus Spenn.), Sage (Salvia officinalis L.), Thyme (Thymus vulgaris L.), and Lavender (Lavandula angustifolia L.). A notable innovation on the farm is the cultivation of Moringa oleifera Lam., an emerging superfood [55]. This farm operates as an environmentally friendly, multifunctional establishment dedicated to soil preservation in Sicily [56].

2.2. Field Data Collection

The use of UAVs has been increasing in tasks such as crop management, estimating biomass, predicting yields, and monitoring plant health. UAV imagery can be used to analyze surface models, evaluate crop height and growth rate, and extract canopy temperature and digital plant counting. These data can be used as reference parameters for implementing spatially variable crop operations using a Decision Support System (DSS), enabling farmers to choose the right time to harvest crops such as rosemary for food and essential oil extraction.

Overall, these technological advancements can provide rapid and on-demand real-time data for smart agriculture practices, benefiting farmers and businesses in the agricultural sector [16].

The flight mission involved the strategic planning of parameters through DJI GS Pro software v2.0.17 (

Table 1. Flight and sensor parameters.

Parameters	Value
Flight height	50 m a.s.l.
Overlap front. and lat	70%
Flight speed	10 m s−1
Camera angle	90°
FOV	62.7°
Flight path	0° to the North
GSD	≈2.6 cm
GNSS mode	RTK

). These parameters encompassed various aspects such as height, speed, direction, and the acquisition details of the cameras, including the sequence of shots and the frontal and lateral overlapping.

To mitigate potential disruptions like shadows and weeds, the flight was meticulously executed at noon, when the sun was positioned at the zenith. Prior to the flight, the entire plot underwent harrowing, and four ground control points (GCPs) were strategically positioned in the field. These GCPs were georeferenced using the GNSS (global navigation satellite system) receiver S7-G by Stonex (Milan, Italy), equipped with a Stonex geodetic antenna. This receiver utilized multiband signals from prominent GNSS satellites, including GPS, GLONASS, Galileo, and Bei Dou. Enhanced accuracy was achieved through Real-Time Kinematic (RTK) differential correction data.

The coordinates of the GCPs were acquired in RTK (real-time kinematic positioning) mode, averaging 60 measurements. Subsequently, under favorable weather conditions characterized by clear skies and low wind speed, the flight commenced along the predefined route and waypoints.

The multispectral images were preprocessed to create a multiband ortho-mosaic. Data processing was performed using Agisoft Metashape Professional (version 1.7.3), which generated 3D and 2D spatial data for GIS applications through photogrammetry. After image alignment, ground control points (GCPs) were added using the WGS84 geographical coordinate system (EPSG: 4326), and GCPs were identified in various photos. Image calibration was performed using the drone’s brightness sensor and a white panel for radiometric calibration. The creation of a dense cloud enabled the generation of the mesh and digital elevation model. Finally, the process of orthorectification and mosaicking produced the final multiband ortho-mosaic.

2.3. Spectral Vegetation Indices

Remote sensing techniques have demonstrated their effectiveness across a range of agricultural applications, such as crop classification, yield prediction, and monitoring plant health and nutrient deficiencies. There is a growing emphasis on precision farming and smart agricultural resource management systems aimed at enhancing productivity, profitability, and environmental sustainability.

This technology plays a critical role in sustainable farming practices, allowing farmers to assess soil and crop health at key growth stages. Early evaluations help optimize fertilizer application, while later assessments support health evaluations and yield predictions. Remote sensors deliver timely data on biophysical indicators and their spatial variations, enabling variable rate technology that customizes fertilizer usage according to specific conditions. Among the essential nutrients, nitrogen is particularly crucial for crop growth, as it affects chlorophyll content and photosynthesis. However, excessive nitrogen can lead to runoff and aquatic pollution, while inadequate levels can diminish crop yields and result in financial losses.

To tackle these challenges, accurately assessing nitrogen levels in fields is essential, particularly during the early stages of crop growth. Remote sensing of vegetation utilizes passive sensors to capture electromagnetic reflectance from plant canopies, with reflectance characteristics varying based on plant type and water content. Healthy plants typically exhibit high reflectance, whereas dry plants display lower reflectance values.

Vegetation indices obtained from reflectance data can be used to evaluate various plant characteristics, such as water content and stress levels, thereby enhancing our understanding of plant dynamics. However, interpreting remote sensing data can be complex and is often constrained by the reliance on a limited number of spectral bands. Despite these difficulties, researchers are developing advanced algorithms and techniques for assessing plant health, showing that even basic vegetation index algorithms can be effective tools for monitoring agricultural health [57].

Remote sensing methods have proven to be valuable for assessing nitrogen status and crop health. Research shows that nitrogen deficiency leads to a decrease in leaf chlorophyll content, which increases transmittance at visible wavelengths and alters the reflectance of crop leaves. These alterations enable the estimation of chlorophyll concentration and nitrogen variability.

As a result, the optical indices employed to estimate chlorophyll levels are affected not only by these additional factors but also by the reflectance of the soil. Recent studies have increasingly concentrated on the connection between the optical properties of vegetation and the concentrations of photosynthetic pigments, especially chlorophyll-a, chlorophyll-b, and carotenoids. These pigments display unique spectral characteristics, with specific absorption features at different wavelengths, which allows remote sensing techniques to assess their effects on vegetation reflectance [23].

This has resulted in the creation of numerous approaches, both empirical and model-based, for estimating chlorophyll content at both the leaf and canopy levels [58,59].

Research has largely focused on employing optical indices to enhance sensitivity to chlorophyll content while minimizing variability from other influences. This has involved assessing reflectance in narrow spectral bands, utilizing band ratios, and analyzing the characteristics of derivative spectra. Important spectral regions for chlorophyll analysis include approximately 680 nm, which is associated with chlorophyll-a absorption, and 550 nm, where chlorophyll absorption is minimal. The existing literature thoroughly discusses the most effective wavelengths and chlorophyll indices [60].

A notable index is the Modified Chlorophyll Absorption in Reflectance Index (MCARI) [61], which is an enhancement of the original Chlorophyll Absorption in Reflectance Index (CARI) [62] (Equation (1)).

$$\mathrm{C}\mathrm{A}\mathrm{R}\mathrm{I}=(\left({700}_{\mathrm{n}\mathrm{m}}-{670}_{\mathrm{n}\mathrm{m}}\right)-0.2\left({700}_{\mathrm{n}\mathrm{m}}-{550}_{\mathrm{n}\mathrm{m}}\right))\left({\displaystyle \frac{{700}_{\mathrm{n}\mathrm{m}}}{{670}_{\mathrm{n}\mathrm{m}}}}\right)$$

(1)

CARI seeks to address the variability in photosynthetically active radiation attributed to non-photosynthetic materials by employing reflectance data from certain wavelengths, specifically 550 nm, 670 nm (the absorption peak for chlorophyll-a), and 700 nm (the transition point between pigment absorption and structural effects). MCARI quantifies chlorophyll absorption depth at 670 nm in relation to reflectance at 550 nm and 700 nm as defined in Equation (2).

$$\mathrm{M}\mathrm{C}\mathrm{A}\mathrm{R}\mathrm{I}=\left({700}_{\mathrm{n}\mathrm{m}}-{670}_{\mathrm{n}\mathrm{m}}\right)-0.2·\left({700}_{\mathrm{n}\mathrm{m}}-{550}_{\mathrm{n}\mathrm{m}}\right)$$

(2)

Although MCARI is effective, it is sensitive to background reflectance, which complicates its interpretation, particularly at low leaf area indices (LAI) [53]. Our investigation of the modified chlorophyll absorption ratio index (MCARI) is motivated by its promising applications in remote sensing for precision agriculture. In contrast to other indices that rely heavily on multiple narrow spectral bands and have a strong correlation with chlorophyll concentration, MCARI offers greater versatility.

To overcome these limitations, the paper employs an enhanced version of the MCARI designed to improve sensitivity at low chlorophyll concentrations. Some researchers have proposed augmenting MCARI with soil-adjusted indices, such as the Optimized Soil-Adjusted Vegetation Index (OSAVI), to reduce the influence of background reflectance and improve the detection of leaf chlorophyll variability [63]. OSAVI is mathematically derived from the Soil-Adjusted Vegetation Index (SAVI) to effectively account for soil effects (Equations (3)–(5)).

$$\mathrm{S}\mathrm{A}\mathrm{V}\mathrm{I}={\displaystyle \frac{\left({800}_{\mathrm{n}\mathrm{m}}-{670}_{\mathrm{n}\mathrm{m}}\right)}{\left({800}_{\mathrm{n}\mathrm{m}}+{670}_{\mathrm{n}\mathrm{m}}+\mathrm{L}\right)}}·(1+\mathrm{L})$$

(3)

L = 0.5

$$\mathrm{O}\mathrm{S}\mathrm{A}\mathrm{V}\mathrm{I}={\displaystyle \frac{\left({800}_{\mathrm{n}\mathrm{m}}-{670}_{\mathrm{n}\mathrm{m}}\right)}{\left({800}_{\mathrm{n}\mathrm{m}}+{670}_{\mathrm{n}\mathrm{m}}+\mathrm{Y}\right)}}$$

(4)

Y = 0.16

$$\mathrm{T}\mathrm{C}\mathrm{A}\mathrm{R}\mathrm{I}=3\left(\left({700}_{\mathrm{n}\mathrm{m}}-{670}_{\mathrm{n}\mathrm{m}}\right.)-0.2({700}_{\mathrm{n}\mathrm{m}}-{550}_{\mathrm{n}\mathrm{m}}){\displaystyle \frac{\left({700}_{\mathrm{n}\mathrm{m}}\right)}{\left({670}_{\mathrm{n}\mathrm{m}}\right)}}\right)$$

(5)

This paper presents the TCARI/OSAVI ratio as an effective approach for accurately estimating crop chlorophyll content through multispectral remote sensing imagery.

2.4. Rosemary and Sage Harvesting Time Individuation, Collecting, and Drying

Chlorophyll is a vital pigment in leaves, essential for photosynthesis by absorbing radiation. Total canopy chlorophyll content is determined by multiplying the leaf chlorophyll concentration by the leaf area index (LAI), a key indicator of vegetation productivity. Monitoring leaf chlorophyll content is crucial in farm management [61,62,63], while total canopy chlorophyll content is closely linked to primary production [64,65,66,67], and crop yields [68,69]. Vegetation indices (VIs) that strongly correlate with canopy chlorophyll content can be used to estimate gross primary production (GPP) in crops. Leaf chlorophyll content, measured in μg cm⁻², is also an important variable in agricultural remote sensing due to its close relationship with leaf nitrogen content.

Remote sensing techniques have become an effective, non-invasive method to assess plant characteristics, using the spectral signatures of canopies, which can be influenced by environmental and agricultural factors. Vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI), are widely used for this purpose because of their ease of calculation and broad applicability. NDVI is effective for estimating leaf chlorophyll content and the extent of photosynthetically active vegetation, as healthy leaves absorb visible red light and reflect near-infrared (NIR) light. NDVI can be derived from aerial imagery captured by platforms like UAVs, which offer flexibility, cost-efficiency, and high spatial resolution (Equation (6)).

$$NDVI={\displaystyle \frac{\left(NIR-RED\right)}{\left(NIR+RED\right)}}$$

(6)

Chlorophyll content in leaves is a primary indicator of greenness, nutritional status, and carbon uptake in forests. Many studies have demonstrated a strong correlation between chlorophyll content and leaf nitrogen levels in crops. Nitrogen fertilizer applications improve chlorophyll content, leaf area index (LAI), and leaf dry weight, while a lack of nitrogen leads to premature leaf senescence and reduced chlorophyll content, as seen in safflower growth after fertilization [70].

Leaf chlorophyll content also serves as a crucial indicator of photosynthetic activity, plant health, stress levels, and nutritional status, making it especially important for precision agriculture. Recent studies indicate that chlorophyll content can be retrieved effectively using hyperspectral vegetation indices from specific reflectance bands. This research evaluates various vegetation indices, including NDVI and modified chlorophyll absorption ratio indices (MCARI, TCARI), and their integrated forms (MCARI/OSAVI and TCARI/OSAVI). These indices have been developed to reduce the influence of atmospheric and soil interference.

Chlorophyll has strong reflectance peaks in the red and blue regions, but the blue peak is not used for estimating chlorophyll due to interference with carotenoid absorption. Maximum absorbance occurs between 660 nm and 680 nm in the red region, but its effectiveness for predicting chlorophyll content is limited at low concentrations, prompting further exploration of other wavelengths for more accurate estimations [22].

At the beginning of the rosemary flowering (27 April 2024) and sage before flowering (24 May 2024), measurements were carried out using a Phantom 4 UAV, equipped with a multispectral camera (DJI, Shenzhen, China). The high-resolution multispectral images were ortho-mosaicked, and thematic maps of NDVI (Normalized Difference Vegetation Index) and TCARI/OSAVI ratio indices were generated to assess the right time for rosemary and sage harvesting. The harvested rosemary and sage were then dried [52,71].

2.5. A Smart Solar Dryer (SSD) WSN-Based System

This drying process involves the use of devices with a heat pump, drying the products at temperatures between 20 and 50 °C with very dry air to maintain their color, aroma, and chemical composition. The energy for the dryer is generated by photovoltaic panels on the roof and facades of the structure, allowing on-site energy exchange and self-consumption. Real-time monitoring of the drying process is enabled through sensors sending data via Wi-Fi to a ThingSpeak account. The system helps in decision-making during the drying process by accurately monitoring moisture loss and drying rates for the herbs.

The drying method used involves the application of forced hot air convection to extract moisture. Herb biomass is distributed over extensive areas in one or more layers, allowing dry air to circulate above it. Throughout the process, the temperature is maintained at 40 °C and the relative humidity at 25%. This technique is designed to reduce the water content of the herbs while preserving their quality by limiting microbial and enzymatic activity (Figure 2) [52,72,73].

2.6. Hygienic and Safety Aspects of Rosemary and Sage

The microbiological assessment of both fresh and dried rosemary and sage was conducted using a culture-dependent approach, following a specific methodology [61]. Briefly, ten grams of each aromatic herb were homogenized in Ringer’s solution (Sigma-Aldrich, Milan, Italy), serially diluted in the same diluent, and placed on selective agar media. Specifically, total mesophilic microorganisms (TMM) on Plate Count Agar (PCA; Biotec, Grosseto, Italy) incubated at 30 °C for 48 h; members of the Enterobacteriaceae family in Violet Red Bile Glucose Agar (VRBGA; Condalab, Madrid, Spain) incubated at 37 °C for 24 h; Coagulase-positive staphylococci (CPS) on Baird Parker agar (BP; Oxoid, Hampshire, UK) incubated at 37 °C for 48 h; Escherichia coli on Hektoen Enteric Agar (HEA; Microbiol Diagnostici, Cagliari, Italy) incubated at 37 °C for 24 h; yeasts on Dichloran-Rose-Bengal Chloramphenicol agar (DRBC; Microbiol Diagnostici) incubated at 30 °C for 48 h; Molds on Potato Dextrose Agar (PDA; Microbiol Diagnostici) incubated at 25 °C for 7 d. Detection of Listeria monocytogenes and Salmonella spp. was performed on 25 g of each herb sample, following the ISO 11290-1 (2017) and ISO 6579-1 (2017) [74,75] guidelines, respectively. All agar media were spread plated, except those used for growing members of the Enterobacteriaceae family, which were pour plated on double-layered agar. The plate counts were incubated aerobically. Analyses were performed in triplicate.

2.7. Statistical Analysis

Microbiological data were analyzed using One-Way Analysis of Variance (ANOVA) with XLStat software version 7.5.2 for Excel (Addinsoft, New York, NY, USA). Differences between fresh and dried rosemary and sage was achieved by Tukey’s test at p < 0.05.

3. Results and Discussion

3.1. Rosemary and Sage Harvesting Time

The collected biomass was intended for the drying process, so it was necessary to identify the time of complete flowering for rosemary and before flowering for sage. The harvested plants belonged to local ecotypes characterized by considerable genotypic variability and non-uniform flowering. The NDVI is sensitive toward crop biophysical properties like nitrogen, chlorophyll, vigor, and biomass, etc. The rosemary was harvested when the NDVI values of most of the plants were equal to 0.64, and high flowering was evident from visual checks (Figure 3). The sage was harvested when the NDVI values of most of the plants were equal to 0.59 (Figure 3).

The analysis demonstrates that the combined use of TCARI and OSAVI is highly effective for estimating crop photosynthetic pigments. Predictive relationships for estimating chlorophyll based on the TCARI/OSAVI ratio were established using above-canopy reflectance data [63]. These results indicate a correlation between chlorophyll content and the slope of TCARI versus OSAVI. As a result, we calculated the TCARI/OSAVI ratio and evaluated its effectiveness in accounting for the influence of soil background reflectance and crop structural development. Our goal was to establish a distinct relationship between chlorophyll content and the TCARI/OSAVI combination. The analyses provided above demonstrate that the integrated application of TCARI and OSAVI holds significant promise for estimating crop photosynthetic pigments. Predictive scaling relationships were developed to estimate chlorophyll levels based on the TCARI/OSAVI ratio obtained from above-canopy reflectance data. The TCARI/OSAVI ratio serves as a spectral indicator of pigment concentrations at the canopy level. By reducing the impact of LAI variability, it demonstrates a strong correlation and a distinct relationship with chlorophyll content.

As shown in Figure 4, the chlorophyll values were high for the low TCARI OSAVI values and high NDVI values.

3.2. Drying Process Results

The volume of water extracted from the product during each drying cycle can be calculated [52]. Approximately 109 liters of water are eliminated in every full-load drying cycle. An absorption machine, with a dehumidification capacity of 52.8 kg per 24 h, is sized according to this volume. Each drying cycle has a duration of 23.5 h, essentially spanning one day. Alongside the absorption machine, a reversible compression heat pump rated at 9000 BTU is employed to regulate the temperature within the drying chamber, maintaining it between 10 and 30 °C. The contribution of the air conditioning unit to dehumidification is not factored into the sizing of the photovoltaic dryer.

The solar dryer is engineered to fulfill its annual energy requirements using electricity generated by photovoltaic panels installed on the roof and facades of the drying structure. The system comprises six modules on the roof and another six on the south elevation, with each module providing 500 W of power. The photovoltaic generator has an annual energy output potential of 8383 kWh, with strings 1 and 2 yielding 4407 kWh and 3976 kWh, respectively. The total electrical energy demand for each drying cycle, which includes operating temperature and humidity sensors, the PLC, and LED lighting, is 190 kWh.

In summary, the photovoltaic dryer is designed to effectively remove moisture from the product while adhering to the renewable energy constraints set by the photovoltaic generator, ensuring a sustainable operational model.

The dehumidification process commenced at 9 a.m. on 31 May 2024 and concluded at 8:30 a.m. on 1 June 2024, lasting less than a day (refer to Figure 5). Initially, the relative humidity (RH) in the drying chamber was 41%, which dropped to 8% after approximately 24 h, during which the door was opened for inspection. The temperature (T °C) started at 22.5 °C, then decreased before gradually rising to 25 °C when the door was opened for checking.

Data collected by sensors were transmitted via Wi-Fi to a ThingSpeak account, allowing for real-time monitoring of the drying process. These data included fluctuations in product moisture content and drying rates. The Wireless Sensor Network (WSN) was instrumental in providing decision support throughout the drying process, enabling precise tracking of moisture loss and drying rates for both rosemary and sage.

At the conclusion of the drying cycle, the moisture content was measured at 15% for sage and 17% for rosemary. The fresh-to-dry weight ratio was 100/41 for sage and 100/62 for rosemary, with the final weights of the dried biomass being 32.6 kg for sage and 63.7 kg for rosemary.

These results are similar to those conducted with solar drying of the medicinal plants integrated with phase change storage material [44,45].

The comparison between SSDs and conventional drying methods for aromatic crops reveals significant advantages of the former in multiple areas. Drying times for basil and sage were substantially reduced using SSD, with times of 58, 46, and 32 h at temperatures of 30, 40, and 50 °C, respectively, compared to 96 h outdoors and 144 h indoors with traditional methods [76]. In terms of energy consumption, SSD demonstrated savings ranging from 25.54% to 77.1% when compared to conventional methods [76], with energy use for lemongrass and lavender recorded at 27.72 kWh and 43.02 kWh, respectively, at 50 ± 2 °C [77]. Furthermore, SSD outperformed conventional methods in terms of product quality, with better color retention (15.60 ± 0.89 for lemongrass) and higher essential oil content for lemongrass (1.96%), thyme (1.73%), marjoram (3.40%), and lavender (2.76%) at 40 °C [77]. SSD also exhibited lower microbial load for basil and sage at 40 °C. From a cost perspective, SSD was more economical, with drying costs per kilogram of chili being 39% lower than those using a cabinet dryer [78]. In conclusion, the SSD method offers superior drying times, energy efficiency, product quality, and cost-effectiveness compared to conventional drying methods for aromatic crops [76,77,78].

3.3. Safety Criteria for Foodstuffs

Both fresh and dehydrated rosemary and sage samples underwent microbiological analysis to assess their suitability for human consumption. This assessment is essential before using aromatic herbs and spices in food applications, as they serve as carriers for foodborne pathogens [79,80]. The investigation specifically targeted the main bacterial pathogens, including members of the Enterobacteriaceae family, E. coli, CPS, Salmonella spp., and L. monocytogenes. These pathogens are well-known causative agents of global foodborne illness outbreaks and pose significant public health risks [81,82]. None of the samples analyzed revealed detectable levels of these pathogens.

These results are undoubtedly due to the high-quality standards followed throughout the entire process, from harvesting to processing [83].

Figure 6 shows the viable counts of TMM and molds present on rosemary and sage before and after the dehydration process. Fresh aromatic herb samples hosted levels of TMM and molds between 10³ and 10⁴ Colony Forming Units (CFU)/g. The presence of these microorganisms likely originates from environmental contamination [84,85].

Statistically significant differences (p < 0.001) were detected among fresh and dried rosemary and sage. Therefore, the drying process completely eradicated TMM and molds from rosemary and sage, rendering their levels undetectable (reported as “2” in Figure 6). From a microbiological standpoint, the dried herbs produced in this study met the specifications set by the International Commission for Spices, Herbs, and Dried Vegetable Seasonings, which establishes a limit of 10⁴ CFU/g total bacteria at 30 °C [86,87].

4. Conclusions

The latest advancements in medicinal and aromatic crops are changing how we perceive and utilize these plants. Sustainable farming practices, genetic enhancements, and investigations into their medicinal qualities are among the innovations driving this transformation. These breakthroughs not only benefit the environment, farmers, and consumers by providing a wider range of top-notch products but also position these crops to be key players in multiple sectors, such as agriculture and healthcare, as the global focus shifts towards sustainability and wellness.

The study highlighted the effectiveness of using the TCARI and OSAVI indices to estimate crop photosynthetic pigments. A strong correlation between chlorophyll content and the TCARI/OSAVI ratio was observed, helping to account for soil reflectance and crop structure. The combined use of these indices showed promise for accurately estimating chlorophyll levels and reducing the impact of LAI variability. High chlorophyll values were linked to low TCARI/OSAVI and high NDVI values.

The SSD prototype is an artificial intelligence (AI)-powered medicinal and aromatic crops (MAPs) smart solar dryer, a highly innovative system designed to enhance the drying process of medicinal and aromatic plants by combining solar drying technology with AI to optimize efficiency, quality, and sustainability. The main benefits are energy efficiency (by leveraging solar energy, the dryer reduces reliance on traditional energy sources and minimizes the carbon footprint), higher quality (the system can fine-tune drying parameters to preserve the nutritional value, color, fragrance, and essential oils of the MAPs, which can degrade if dried improperly), cost-effectiveness (reducing energy consumption and enhancing drying quality minimizes crop loss and improves profitability), real-time monitoring and control (allows for remote monitoring and control through apps or web dashboards, offering flexibility to farmers, allowing them to track the drying process and intervene if necessary), and automation (it reduces manual intervention, saving labor costs and improving precision in the drying process).

Data from sensors was transmitted via Wi-Fi to ThingSpeak for real-time monitoring, providing insights into moisture content and drying rates. These results were similar to those obtained from solar drying of medicinal plants with phase change storage material.

Regarding the microbiological findings, the application of a drying process in a smart solar system represents a winning strategy to prevent microbial growth.

With precision and smart technologies applied to these aromatic crop farms paving the way, the future looks promising for these versatile and valuable plants, offering new opportunities in healthcare, agriculture, nutraceutical, cosmetic and food sectors.