AgriEngineering MDPI

🌊 🤖 A Simple Model for Estimating Reservoir Evaporation in Managing Water for Irrigation Using Remote Sensing and Ground Measurements.

Reservoirs are essential for storing irrigation water in arid regions such as the southwestern United States. However, a significant portion of this water is lost each day to evaporation, especially during the hot, windy summer months. Consequently, less water is available for crop irrigation and other needs. Measuring evaporation losses across an entire reservoir is difficult because traditional methods rely on expensive instruments that capture evaporation only over a small footprint. In this study, we developed a simple satellite-based model to estimate daily evaporation across the entire reservoir using satellite imagery and weather data. The Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE) model approach combines satellite-derived water surface temperature with weather observations, including air temperature, humidity, and wind speed.

The method was first evaluated at Elephant Butte and Caballo Reservoirs in the Rio Grande’s (River) main channel in New Mexico, USA, where evaporation measurements were also conducted. These reservoirs play an important role in supporting irrigation and other uses. They are part of the ‘Rio Grande Project’ in south-central New Mexico. They store water to irrigate about 55,000 hectares (136,000 acres) of land along the river valley, including land in El Paso County, Texas, USA, and land in the Republic of Mexico bordering the USA. The method presented was then evaluated at several additional lakes and reservoirs across different climates worldwide, including those in the United States, Brazil, China, Sweden, and New Zealand. Results showed that the model estimated evaporation with high accuracy when compared with field measurements collected using advanced monitoring systems.

The study also showed that satellite observations can successfully capture seasonal and spatial changes in evaporation across reservoirs. One of the key advantages of this approach is its simplicity. Unlike many complex evaporation models, it requires fewer input variables while still providing reliable evaporation estimates. This work demonstrates how satellite technology can help agricultural communities better manage limited water supplies, improve irrigation planning, support drought preparedness, and make smarter decisions about reservoir operations in a warming and increasingly water-scarce climate.

📖 Read more: https://brnw.ch/21x36QE🚜 The authors: Thanushan Kirupairaja and A. Salim Bawazir

🌱 Automated laser analysis of seeds

The quality of seeds is directly linked to efficient agricultural production. The monitoring of seed quality can be provided by the laser speckle phenomenon, known as biospeckle laser.

The automation of the laser speckle protocol set in a designed carousel associated with docks filled with a gel to keep the moisture of the seed and with a humidity control system was the main contribution of this work.

Up to eight seeds can be analysed in a batch, allowing detailed studies of the cellular activity in time by the laser technique (the number of slots in the carousel can be easily enlarged). The automated system synchronizes the seed positioning, the laser activation, and the image capture by a digital camera. Subsequently, these images are processed using the biospeckle laser technique.

In this work, maize seeds were tested during 36h of imbibition, with four hours of interval between each illumination and image acquisition. The test consisted of their illumination by a green laser, and their speckle images were collected for analysing the biological activity. The equipment, carousel, and electric motor were placed in a case where the humidity was controlled by an independent microprocessor. We could follow the evolution of the biological activities of the seeds during the 36 hours of imbibition until the protrusion for those seeds with high vigour. The automated carousel, set with the laser, optics, and camera, carried out the planned illumination in time successfully, and presented reliable results for seed analysis in an autonomous way, improving the efficiency of seed analysis.

🔍 Read full article: https://brnw.ch/21x354z🚜 The authors: José L. Contado, Dimitri Viana, Bruno Vicentini, Antônio A. A. Chepluki, and Roberto A. Braga

🌱 🤖 Seeing Soil Problems from Above: How Drones Can Help Black Pepper Farmers

Black pepper is an important crop in the Brazilian Amazon, especially in Pará, where it supports local production systems and family farming. However, even when fertilization and irrigation are well managed, soil physical conditions can still limit plant development. One of the main problems is soil compaction, which increases soil bulk density, reduces pore space, restricts root growth, and can lower plant vigor.
In this study, we evaluated a five-year-old black pepper plantation in Baião, Pará, Brazil, using field soil measurements and drone imagery. Soil samples were collected at 100 georeferenced points and at four depths: 0–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. A drone equipped with a multispectral camera was used to calculate the Normalized Difference Vegetation Index, known as NDVI, which indicates plant vigor based on canopy reflectance.

The study combined two approaches: kriging, used to map soil bulk density across the field, and Bivariate Moran’s I, used to identify where soil compaction and plant vigor were spatially related. Soil bulk density ranged from 1.14 to 1.80 Mg m⁻³, while NDVI ranged from 0.07 to 0.91. The results showed a clear inverse spatial association: areas with higher soil bulk density tended to have lower NDVI, suggesting that soil physical limitations may reduce crop vigor.

This approach made it possible to identify priority zones for localized management. Areas with compacted soil and low plant vigor require targeted interventions, such as adding organic matter or using cover crops like Crotalaria juncea. In contrast, areas with low compaction and high vigor can serve as examples of good management.

Overall, the integration of soil sampling, drone imagery, and spatial analysis offers a practical tool to support precision management in black pepper fields, helping farmers make more efficient, site-specific decisions.

🤖 Explore: https://brnw.ch/21x32Mv

🚜 The authors: Nelson Ken Narusawa Nakakoji, Ítala Duam Souza Narusawa, Fábio Júnior de Oliveira, Welliton de Lima Sena, Félix Lélis da Silva, Gabriel Garreto dos Santos, João Paulo Ferreira Neris, Pedro Guerreiro Martorano, Alexandre da Trindade Lélis, Jose Gilberto Sousa Medeiros, Norberto Cornejo Noronha, Luís Sérgio Cunha Nascimento, Everton Cardoso Wanzeler, Jean Marcos Corrêa Tocantins, Thais Lopes Vieira, João Fernandes da Silva Júnior, and Paulo Roberto Silva Farias

🍃 🌊 Wind is one of the main reasons why sprinkler irrigation does not always apply water evenly. In traveling-gun systems, droplets travel long distances through the air, making them especially sensitive to wind speed and wind direction. When water is pushed away from the intended strip, some areas receive too little water while others receive too much, reducing irrigation efficiency and making field management more difficult.

In our study, we evaluated how daytime and nighttime wind conditions affect the performance of traveling-gun sprinklers. We combined observed water distribution patterns, local wind records, and irrigation simulations to test different towpath spacings and wind directions. The results showed a clear practical message: irrigation performed better at night. On average, nighttime operation increased the Christiansen uniformity coefficient by about 9.5 percentage points compared with daytime operation. This improvement was mainly associated with calmer and more favorable nighttime wind conditions.

The study also showed that wind direction matters as much as wind speed. When the wind blew parallel to the towpath, the effective irrigated swath became narrower, and uniformity dropped sharply, especially under stronger winds. At 65.6% of the wetted diameter, increasing wind speed from 0 to 6 m s⁻¹ reduced uniformity from 84.4% to only 28.6% when the wind was parallel to the towpath. Under the same wind speed, uniformity remained much higher when the wind was perpendicular to the towpath, reaching 76.1%.

These findings provide practical guidance for farmers and irrigation managers: whenever possible, schedule traveling-gun irrigation during nighttime periods, avoid towpaths aligned with prevailing winds, and use tighter towpath spacing under windy conditions. Considering wind direction—not only wind speed—can help improve water distribution and make irrigation planning more reliable.

📖 Read more: https://brnw.ch/21x2TtV

🧑‍🌾 The authors: Henrique Fonseca Elias de Oliveira, José Henrique Nunes Flores, Lessandro Coll Faria, Samuel Beskow, Giuliani do Prado, Gustavo Borges Lima, Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva, and Alberto Colombo

🌱 Smarter Rice Yield Prediction Using Satellites and Crop Models

Rice feeds more than half of the world’s population, making accurate yield prediction essential for food security, water management, and climate resilience. Traditional field surveys are often slow and limited, especially across large agricultural regions. Our review highlights how combining satellite remote sensing with crop simulation models can provide faster, more reliable, and large-scale rice yield estimation.

Satellite technologies such as Sentinel-1, Sentinel-2, MODIS, and hyperspectral sensors can continuously monitor rice fields by capturing information related to crop growth, canopy condition, moisture status, and plant health. At the same time, crop simulation models like DSSAT, APSIM, ORYZA, and WOFOST help simulate how rice responds to weather, soil, water, and management practices.

Our study found that integrating these two technologies significantly improves yield prediction accuracy compared to using either method alone. In particular, radar-based satellite data are highly useful in monsoon regions because they can collect information even under cloudy conditions. Advanced approaches such as data assimilation, machine learning, and hybrid modelling are opening new opportunities for early-season forecasting and climate-smart agriculture.

The review also discusses current challenges, including limited field data, model calibration issues, and differences in spatial scale. Despite these limitations, integrated remote sensing and crop modelling approaches offer a powerful pathway for improving agricultural planning, supporting farmers, and strengthening food security under changing climate conditions.

This work demonstrates how digital agriculture tools can help build smarter, more resilient rice production systems for the future.

🔍 Learn more: https://brnw.ch/21x2Php

🧑‍🌾 The authors: Chilakamari Lokesh, Murali Krishna Gumma, R. Susheela, Swarna Ronanki, M. Shankaraiah, and Pranay Panjala

🤖 Smart Drones and Technology for Healthier Crops: The Future of Mixed Cropping

If you grow coffee, plantain, and avocado on the same farm, you already know that each crop requires different care. Which plants are under water stress? Which ones need more nutrients? Answering these questions by manually inspecting every plant is expensive, slow, and inaccurate.

Traditional crop monitoring methods rely heavily on manual labor. Farmers walk through the fields, visually inspect crops, and take notes. This approach is:
• Expensive: requires significant labor
• Inaccurate: problems can easily go unnoticed
• Inefficient: impractical for large farms or complex systems

In polyculture systems (where multiple crop species grow together), the challenge becomes even greater. Each crop has different water and nutrient requirements, and treating the entire field as a single unit can lead to costly mistakes.

Researchers from the Universidad Tecnológica de Pereira in Colombia have developed an integrated system that combines:

1. Drones with Multispectral Cameras
• Capture images across multiple spectral bands, including near-infrared
• Detect plant health conditions invisible to the human eye
• Cover large agricultural areas in just minutes

2. Artificial Intelligence (Deep Learning)
• Automatically identifies each crop species in the images
• Distinguishes coffee, plantain, and avocado crops
• Detects water and nutrient stress for each specific crop

3. Ultra-Fast Storage and Retrieval
• Efficiently stores millions of images
• Retrieves photos from any farm location within milliseconds
• Calculates crop-specific health indices such as NDVI

The system was validated on a 20,000 m² farm in Pereira, Colombia, containing coffee, plantain, and avocado crops:

• 82.3% accuracy in crop species identification
• Ultra-fast image retrieval (reducing computational complexity from O(n²) to O(log n))
• Crop-specific analysis: each species receives a personalized diagnosis
• Scalable architecture capable of expanding to larger and more complex farms

With this technology, farmers can achieve:
• Smart irrigation: water only the plants that truly need it • Precision fertilization: apply nutrients exactly where required • Early problem detection: identify pests or stress before they spread
• Data-driven decisions: generate clear reports on crop health
• Higher productivity: less waste and greater yield per square meter

The future of agriculture is not about working harder — it is about working smarter.

📖 Read more: https://brnw.ch/21x2NM6
🚜 The authors: Oscar Andrés Martínez, Kevin David Ortega Quiñones, and German Andrés Holguin-Londoño

🚜 New Tools to Sample Walnuts and Rapidly Measure Moisture in a Commercial Huller

Walnut drying hasn’t changed much in the last 100 years. Freshly hulled walnuts are dried by blowing hot air through large bins that hold about five tons of nuts. Drying usually takes about 12 to 24 hours.

To better understand walnut drying and reduce energy use, researchers need ways to easily collect samples from throughout the bins and rapidly measure moisture. Current sampling methods are labor‑intensive and slow, making it difficult to study how moisture moves during drying.

In this project, a new walnut sampling system was developed to collect samples at different locations and depths in a drying bin. The system uses hollow square columns with gate valves and buckets. To collect the samples, the gates were briefly opened, walnuts fell into small buckets below the gates, and the buckets were then lifted out of the column.

Two rapid moisture‑measurement methods were also developed: one measuring weight loss after rapid oven‑drying and another using near‑infrared (NIR) technology. For the rapid oven-drying method, walnut samples were ground, and a 12‑gram portion was weighed, dried at 105°C for three hours, and weighed again to determine moisture loss. The rapid oven-drying method was within 1.5% (dry basis) moisture of the standard 24‑hour oven-drying method. The NIR method also provided good moisture prediction data.

The moisture data collected at different locations and depths using these new tools showed that walnuts near the bottom of the bin dried faster than those near the top. If the drying region moves too slowly through the bin, there is a possibility that the walnuts near the air inlet can become overdried.

The new sampling system and rapid moisture‑testing methods helped researchers better understand how moisture loss happens in commercial walnut drying bins. These new tools can also support future research aimed at improving energy efficiency in a commercial walnut hulling facility.

📖 Read more: https://brnw.ch/21x2IBW

🧑‍🌾 The authors: Jaya Shankar Tumuluru, Paul A. Funk, Ronald P. Haff, Andrew Paul Breksa III, Joseph S. McIntyre, Kathleen M. Yeater, Derek P. Whitelock, Carlos B. Armijo, Yuzhu Zhang, and Wally Yokoyama

🚜 Modern farms rely on greenhouses, sensors, pumps, valves, and small machines that must work every day. When a small plastic part breaks or a special component is not available, whole systems can stop, costing time and money. Our recent study in AgriEngineering shows how low‑cost 3D printing can help farmers, technicians, and students rapidly create and repair such parts—right in the workshop or lab.

Using common desktop 3D printers and affordable plastic filaments, we designed and built real examples that are directly useful in agriculture. These include safety covers for damaged electric water pumps, joints for assembling lightweight greenhouse structures, custom bases for GNSS antennas, housings that protect temperature and humidity sensors, servo‑driven irrigation valves, and gentle fruit‑harvesting grippers that reduce bruising. All of these were tested in practice to check fit, function, and durability.

The idea is simple: start from a specific need in the field, such as a missing cover, a valve you want to automate, or a special joint that does not exist in the market. Then design or 3D‑scan the part, print a cheap prototype in basic plastic (PLA), improve the design if needed, and finally reprint it in a more durable material, like ABS, when the part must work outdoors in the sun and heat. This cycle lets users move quickly from idea to working solution without waiting for suppliers or expensive spare parts.

We also collected feedback from 100 people connected to agriculture—students, professors, and farmers—who used or evaluated these parts. Most were positive about the potential of 3D printing to support repairs, small upgrades, and learning activities, but they also raised important concerns: the process can feel complex at first, and standard plastics like PLA may not last long under strong sunlight, high temperatures, and demanding mechanical loads. These comments underline a key message of our work: 3D printing is not a magic bullet but a powerful tool when combined with good design, proper material choice, and realistic expectations.

Looking ahead, we see great opportunities for using recycled plastics and stronger, weather‑resistant materials to make 3D‑printed parts more sustainable and robust in the field. Open‑source digital designs, shared online, can help farmers and technicians everywhere print their own spare parts, adapt tools to local needs, and keep critical systems—like irrigation and protected cultivation—running with less downtime and lower cost. Our study offers practical examples and step‑by‑step experience to help others start using 3D printing as a hands‑on ally for smarter, more resilient agriculture.

📖 Read more: https://brnw.ch/21x2FfK

🚜 The authors: Ioannis-Vasileios Kyrtopoulos, Dimitrios Loukatos, Emmanouil Zoulias, Chrysanthos Maraveas, and Konstantinos G. Arvanitis

🥚 Smarter Egg Sorting: How Artificial Intelligence and Machine Vision Can Improve Egg Quality Control

Every day, millions of eggs are sorted and packaged around the world. Traditionally, this process relies mainly on a single parameter — egg weight. However, eggs with the same weight can still differ significantly in freshness, shell condition, density, and shape. This means that conventional sorting systems may overlook important quality differences.
Our research presents a new intelligent rotary system designed for automatic non-destructive egg quality assessment and classification. Instead of evaluating eggs only by weight, the developed system simultaneously analyzes several physical and geometric characteristics, including egg dimensions, shape, volume, density, and contour properties. The technology combines machine vision, digital image processing, sensors, and fuzzy logic algorithms within a single automated platform.
The experimental study included 750 chicken eggs covering different commercial categories and shape variations. Each egg was analyzed using a camera-based vision module and a precision weighing system. Special software calculated geometric parameters such as longitudinal diameters, cross-sectional area, perimeter, estimated volume, and density. The system then used an intelligent fuzzy logic model to classify eggs into quality categories.
One of the most interesting findings was that egg density behaves as an independent quality parameter. Eggs with very similar weight often showed different density values, which may reflect differences in internal structure and air cell size. This confirms that weight alone is insufficient for reliable quality evaluation.
The developed rotary installation performs all measurements and classification operations within approximately one second per egg. The system automatically moves eggs through weighing, optical analysis, and sorting zones using a rotating drum mechanism. Depending on the calculated quality profile, eggs are directed into the appropriate category channel.
Unlike rigid traditional sorting systems, the proposed fuzzy logic model allows smooth classification decisions in boundary situations where egg categories overlap. This significantly improves sorting stability and reduces classification uncertainty.
The study demonstrates that combining machine vision with intelligent multi-criteria analysis can improve the reliability and flexibility of industrial egg grading systems. Such technologies may help poultry farms and food processing enterprises achieve more consistent product quality, reduce manual labor, and improve automation efficiency.

🔍 Learn more: https://brnw.ch/21x2D8W
🚜 The authors: Jakhfer Alikhanov, Aidar Moldazhanov, Akmaral Kulmakhambetova, Dmitriy Zinchenko, Tsvetelina Georgieva, Eleonora Nedelcheva, and Plamen Daskalov

🌱 Microbial Allies: How to Help Peas Grow with Less Water and Fertilizer?

Did you know that a successful pea harvest begins long before the first leaves appear? It all starts with the seed's strength to germinate, even when the weather is far from ideal. In our recent research conducted in the Amazonas region of Peru, we tested how "invisible allies"—beneficial microorganisms—can help pea seeds (Pisum sativum L.) overcome tough conditions like water scarcity and reduced chemical fertilization.

The Challenge of Water and Soil Farmers increasingly face unexpected droughts (osmotic stress) and the high cost of chemical fertilizers. Our study evaluated whether using bio-inoculants (such as Rizoplant, Amysub, and Trichops) could maintain plant productivity even when only 75% of the usual fertilizer dose was applied.

What Did We Discover?
🌱 Strength from the Start: In the lab, we observed that while a lack of water delays germination, using inoculants like Rizoplant helped seeds maintain better vigor. Treated seeds were "readier" to face drought!
🌱 Field Results: We took the experiment to two different locations: Lámud and Molinopampa. We found that the environment (where you plant) drastically influences yield. For instance, plants in Molinopampa were significantly taller and more productive than those in Lámud, regardless of the fertilization regime.
🌱 Potential Savings: Most interestingly for growers, yields did not drop drastically when chemical fertilization was slightly reduced and supported by bio-inoculants. This opens the door to more sustainable and cost-effective agriculture.

Why Does This Matter?
This study demonstrates that integrating microorganisms into crop management is not just an eco-friendly alternative but a strategic tool to strengthen pea resilience in the Peruvian highlands. For the farmer, this means that prioritizing soil health and choosing the right inoculant can make a real difference during low-rainfall years.

🔎 Learn more: https://brnw.ch/21x2u1K
🚜 Authors: Francisco Guevara-Fernández, Sebastian Casas Niño, Milagros Ninoska Munoz-Salas, Wagner Meza-Maicelo, Manuel Oliva-Cruz, and Flavio Lozano-Isla