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15 pages, 5022 KiB

Open AccessArticle

Development of a Precision Feeding System with Hierarchical Control for Gestation Units Using Stalls

by Jingjing Xia, Jichen Xu, Zhixiong Zeng, Enli Lv, Feiren Wang, Xinyuan He and Ziwei Li

Appl. Sci. 2023, 13(21), 12031; https://doi.org/10.3390/app132112031 - 4 Nov 2023

Cited by 1 | Viewed by 1983

Abstract

To obtain good productive performance, sows have different nutrition requirements at different gestation periods. However, in gestation stalls, conventional feeders have large relative errors, management is difficult because of the large numbers of sows, and there are shortcomings in feeding precision and data [...] Read more.

To obtain good productive performance, sows have different nutrition requirements at different gestation periods. However, in gestation stalls, conventional feeders have large relative errors, management is difficult because of the large numbers of sows, and there are shortcomings in feeding precision and data management. In order to achieve precision feeding and enhance the control of multiple feeders for gestating sows housed in stalls, this study was carried out to investigate a precision feeding system that could be controlled at multiple levels. This system consisted of an electronic sow feeder (ESF), controller area network (CAN), personal digital assistant (PDA), central controller, and Internet of Things platform (IoTP). The results of the experiment showed that relative errors of 60 ESFs delivering feed were within ±2.94%, and the coefficient of variation was less than 1.84%. When the received signal strength indicator (RSSI) ranged from −80 dbm to −70 dbm, the packet loss rate of the PDA was 3.425%. When the RSSI was greater than −70 dbm, no packet loss was observed, and the average response time was 556.05 ms. The IoTP was at the performance bottleneck when the number of concurrent threads was greater than 1700. These experimental results indicated that the system was not only highly accurate in delivering feed, but was also highly reliable in the transmission of information, and therefore met the production requirements of an intensive gestation house. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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13 pages, 2907 KiB

Open AccessArticle

Comparison of the Spray Effects of Air Induction Nozzles and Flat Fan Nozzles Installed on Agricultural Drones

by Seung-Hwa Yu, Yeongho Kang and Chun-Gu Lee

Appl. Sci. 2023, 13(20), 11552; https://doi.org/10.3390/app132011552 - 22 Oct 2023

Cited by 5 | Viewed by 2838

Abstract

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift [...] Read more.

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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13 pages, 3781 KiB

Open AccessArticle

Determination of the Dependences of the Nutritional Value of Corn Silage and Photoluminescent Properties

by Dmitriy Y. Pavkin, Mikhail V. Belyakov, Evgeniy A. Nikitin, Igor Y. Efremenkov and Ilya A. Golyshkov

Appl. Sci. 2023, 13(18), 10444; https://doi.org/10.3390/app131810444 - 18 Sep 2023

Cited by 3 | Viewed by 1818

Abstract

This article examines existing optical methods for the diagnostics of food and feed products used in agriculture to determine their nutritional value or detect maximum permissible indicators. Among the most common feeds used for cattle, corn silage is considered. Its nutritional value depends [...] Read more.

This article examines existing optical methods for the diagnostics of food and feed products used in agriculture to determine their nutritional value or detect maximum permissible indicators. Among the most common feeds used for cattle, corn silage is considered. Its nutritional value depends on many external factors that need to be taken into account when formulating feeding rations. This article is dedicated to assessing the prospects of using visible-range photoluminescence for determining dry matter content, total protein content, and NDF (neutral detergent fiber) using a portable device in field conditions without lengthy sample preparation. This research aims to develop a laboratory device and establish the theoretical foundations for determining the nutritional value of agricultural feeds using photoluminescence. The study revealed that the most indicative range for measuring nutritional corn silage is to use excitation via radiation with a wavelength of about 362 nm. At the same time, the luminescent radiation flux must be measured in a range of 440–620 nm. Moreover,

R^{2}

values greater than

0.8

were achieved in correlation after constructing luminescence relationships only for the determination of dry matter content/moisture, total protein content, and NDF. This indicates the potential use of the proposed method for determining these parameters. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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14 pages, 3280 KiB

Open AccessArticle

A Miniaturized and Low-Cost Near-Infrared Spectroscopy Measurement System for Alfalfa Quality Control

by Candela Melendreras, Ana Soldado, José M. Costa-Fernández, Alberto López and Francisco Ferrero

Appl. Sci. 2023, 13(16), 9290; https://doi.org/10.3390/app13169290 - 16 Aug 2023

Cited by 1 | Viewed by 1817

Abstract

Food safety and quality are the first steps in the food chain. This work reports a miniaturized, low-cost, and easy-to-use near-infrared spectroscopy (NIRS) measurement system for alfalfa quality control. This is a significant challenge for dairy farm technicians and producers who need rapid [...] Read more.

Food safety and quality are the first steps in the food chain. This work reports a miniaturized, low-cost, and easy-to-use near-infrared spectroscopy (NIRS) measurement system for alfalfa quality control. This is a significant challenge for dairy farm technicians and producers who need rapid and reliable knowledge of the forage quality on their farms. In most cases, the instrumentation suitable for these specifications is expensive and difficult to operate. The core of the proposed NIR spectroscopy measurement system is Texas Instruments’ NIRscan Nano evaluation module (EVM) spectrometer. This module has a large sensing area and high resolution, suitable for forage samples. To evaluate the feasibility of the prototype for analyzing agrifood samples, different ways of presenting the sample, intact or ground, were tested. The final objective of the research is not just to check the efficiency of the proposed system. It is also to determine the characteristics of the measurement system, and how to improve them for alfalfa quality control. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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16 pages, 4051 KiB

Open AccessArticle

Real-Time Deployment of MobileNetV3 Model in Edge Computing Devices Using RGB Color Images for Varietal Classification of Chickpea

by Dhritiman Saha, Meetkumar Pareshbhai Mangukia and Annamalai Manickavasagan

Appl. Sci. 2023, 13(13), 7804; https://doi.org/10.3390/app13137804 - 2 Jul 2023

Cited by 4 | Viewed by 3434

Abstract

Chickpeas are one of the most widely consumed pulses globally because of their high protein content. The morphological features of chickpea seeds, such as colour and texture, are observable and play a major role in classifying different chickpea varieties. This process is often [...] Read more.

Chickpeas are one of the most widely consumed pulses globally because of their high protein content. The morphological features of chickpea seeds, such as colour and texture, are observable and play a major role in classifying different chickpea varieties. This process is often carried out by human experts, and is time-consuming, inaccurate, and expensive. The objective of the study was to design an automated chickpea classifier using an RGB-colour-image-based model for considering the morphological features of chickpea seed. As part of the data acquisition process, five hundred and fifty images were collected per variety for four varieties of chickpea (CDC-Alma, CDC-Consul, CDC-Cory, and CDC-Orion) using an industrial RGB camera and a mobile phone camera. Three CNN-based models such as NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 were evaluated using a transfer-learning-based approach. The classification accuracy was 97%, 99%, and 98% for NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 models, respectively. The MobileNetV3 model was used for further deployment on an Android mobile and Raspberry Pi 4 devices based on its higher accuracy and light-weight architecture. The classification accuracy for the four chickpea varieties was 100% while the MobileNetV3 model was deployed on both Android mobile and Raspberry Pi 4 platforms. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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17 pages, 2981 KiB

Open AccessArticle

UAV Hyperspectral Characterization of Vegetation Using Entropy-Based Active Sampling for Partial Least Square Regression Models

by Donato Amitrano, Luca Cicala, Marco De Mizio and Francesco Tufano

Appl. Sci. 2023, 13(8), 4812; https://doi.org/10.3390/app13084812 - 11 Apr 2023

Cited by 6 | Viewed by 1523

Abstract

Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In [...] Read more.

Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on supervised frameworks and confirmed the suitability of the proposed methodology with operational environments. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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14 pages, 5660 KiB

Open AccessArticle

An Improved Method to Obtain Fish Weight Using Machine Learning and NIR Camera with Haar Cascade Classifier

by Samuel Lopez-Tejeida, Genaro Martin Soto-Zarazua, Manuel Toledano-Ayala, Luis Miguel Contreras-Medina, Edgar Alejandro Rivas-Araiza and Priscila Sarai Flores-Aguilar

Appl. Sci. 2023, 13(1), 69; https://doi.org/10.3390/app13010069 - 21 Dec 2022

Cited by 9 | Viewed by 3517

Abstract

The calculation of weight and mass in aquaculture systems is of great importance, since with this task, it is decided when to harvest; generally, the above is manipulating the body manually, which causes stress in the fish body. Said stress can be maintained [...] Read more.

The calculation of weight and mass in aquaculture systems is of great importance, since with this task, it is decided when to harvest; generally, the above is manipulating the body manually, which causes stress in the fish body. Said stress can be maintained in the fish body for several hours. To solve this problem an improved method was implemented using artificial intelligence, near-infrared spectroscopy camera, Haar classifiers, and a mathematical model. Hardware and software were designed to get a photograph of the fish in its environment in real conditions. This work aimed to obtain fish weight and fish length in real conditions to avoid the manipulation of fish with hands for the process mentioned, avoiding fish stress, and reducing the time for these tasks. With the implemented hardware and software adding an infrared light and pass band filter for the camera successfully, the fish was detected automatically, and the fish weight and length were calculated moreover the future weight was estimated. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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20 pages, 4235 KiB

Open AccessArticle

Digital Transformation of Beekeeping through the Use of a Decision Making Architecture

by Jean-Charles Huet, Lamine Bougueroua, Yassine Kriouile, Katarzyna Wegrzyn-Wolska and Corinne Ancourt

Appl. Sci. 2022, 12(21), 11179; https://doi.org/10.3390/app122111179 - 4 Nov 2022

Cited by 5 | Viewed by 5656

Abstract

The use of information and communication technologies (ICT) in agriculture is far from their potential. In this article, we consider how to facilitate and systematize the process of transforming traditional agriculture into digital agriculture; Agriculture 4.0. Among the different technologies, we focus on [...] Read more.

The use of information and communication technologies (ICT) in agriculture is far from their potential. In this article, we consider how to facilitate and systematize the process of transforming traditional agriculture into digital agriculture; Agriculture 4.0. Among the different technologies, we focus on the IoT aspects. In the article, we propose a new approach for the design of intelligent agricultural management and supervision systems. The proposed approach is illustrated as an example of application in the beekeeping sector. Indeed, this sector is affected by a crisis due to the disappearance of bees and the different actors need support to make their decisions. As an example of decisions that can be made, we can cite: treatment planning or policy planning. An architecture based on sensors and open data is proposed to help them make decisions. An implementation of it is shown; it is based on a device with sensors, as well as an interface to collect the data on beehives and show notifications and alerts to beekeepers. The proposed architecture is flexible, and it can be used in the context of different levels of technology maturity. The final objective is to develop a reusable architecture for Agriculture 4.0. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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14 pages, 17561 KiB

Open AccessArticle

A Visual Feedback for Water-Flow Monitoring in Recirculating Aquaculture Systems

by Krzysztof Okarma, Piotr Lech, Darius Andriukaitis, Dangirutis Navikas, Agata Korzelecka-Orkisz, Adam Tański and Krzysztof Formicki

Appl. Sci. 2022, 12(20), 10598; https://doi.org/10.3390/app122010598 - 20 Oct 2022

Cited by 4 | Viewed by 1908

Abstract

The optimal water flow in fish breeding tanks is one of the crucial elements necessary for the well-being and proper growth of fish, such as salmon or trout. Considering the round tanks and the uneven distribution of water-flow velocity, ensuring a nearly optimal [...] Read more.

The optimal water flow in fish breeding tanks is one of the crucial elements necessary for the well-being and proper growth of fish, such as salmon or trout. Considering the round tanks and the uneven distribution of water-flow velocity, ensuring a nearly optimal flow is an important task that may be performed using various sensors installed to monitor the water flow. Nevertheless, observing the rapid development of video analysis methods and considering the increasing availability of relatively cheap cameras, the use of video feedback has become an interesting alternative that limits the number of sensors inside the water tanks in accordance with the requirements of fish breeders. In this paper, an analysis of the use of optical flow algorithms for this purpose is performed and an estimation method based on their features is proposed. The results of the flow estimation using the proposed method are verified experimentally and compared with the measurement results obtained using the professional water-flow meter, demonstrating a high correlation, exceeding 0.9, confirming the proposed solution as a good alternative in comparison to the use of expensive sensors and meters. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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24 pages, 4805 KiB

Open AccessArticle

The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey

by Federico Angelo Maffezzoli, Marco Ardolino and Andrea Bacchetti

Appl. Sci. 2022, 12(18), 9215; https://doi.org/10.3390/app12189215 - 14 Sep 2022

Cited by 8 | Viewed by 2626

Abstract

This paper investigates how much Italian farms are involved in the so-called “Agriculture 4.0” (Agri 4.0) journey. The paper focuses on analyzing the knowledge and adoption levels of specific 4.0-enabling technologies while also considering the main benefits and obstacles. A descriptive survey was [...] Read more.

This paper investigates how much Italian farms are involved in the so-called “Agriculture 4.0” (Agri 4.0) journey. The paper focuses on analyzing the knowledge and adoption levels of specific 4.0-enabling technologies while also considering the main benefits and obstacles. A descriptive survey was carried out on a total of 670 respondents related to agricultural companies of different sizes. The findings from the survey demonstrate that Italian farms are in different positions in their journey toward the Agri 4.0 paradigm, mainly depending on their size in terms of revenues and land size. Furthermore, there are strong differences concerning both the benefits and obstacles related to the adoption of the Agri 4.0 paradigm, here depending on the technology adoption level. Regarding future research, it would be interesting to carry out the same study in other countries to make comparisons and suitable benchmark analyses. Although scholars have debated about the adoption of technologies and the benefits related to the Agri 4.0 paradigm, to the best of the authors’ knowledge, no empirical surveys have been carried out on the adoption level of digital solutions in agriculture in specific countries. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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16 pages, 4228 KiB

Open AccessArticle

Bench Test and Analysis of Cleaning Parameter Optimization of 4 L-2.5 Wheat Combine Harvester

by Peng Liu, Xiangyou Wang and Chengqian Jin

Appl. Sci. 2022, 12(18), 8932; https://doi.org/10.3390/app12188932 - 6 Sep 2022

Cited by 1 | Viewed by 1640

Abstract

Inaccurate and untimely adjustments of cleaning parameters during the operation of wheat combine harvesters lead to high cleaning losses and impurity rates. For this reason, a self-made 4 L-2.5 threshing and cleaning experiment table was employed for cleaning parameter optimization experiments for wheat [...] Read more.

Inaccurate and untimely adjustments of cleaning parameters during the operation of wheat combine harvesters lead to high cleaning losses and impurity rates. For this reason, a self-made 4 L-2.5 threshing and cleaning experiment table was employed for cleaning parameter optimization experiments for wheat combine harvesters in this paper. The influence of the cleaning parameters on the cleaning loss and impurity rates was analyzed, and the optimum combination of cleaning parameters was predicted and verified. The contribution hierarchy of cleaning parameters to cleaning loss rate is as follows: crank speed of shale shaker > opening of chaffer > operation speed > fan speed > throttle opening. Meanwhile, the contribution hierarchy of cleaning parameters to impurity rate is as follows: operation speed > fan speed > throttle opening > crank speed of shale shaker > opening of chaffer. The predicted optimum combination of cleaning parameters, i.e., when the cleaning loss and impurity rates are both at a minimum and the feed quantity is at the maximum, is as follows: operating speed—2.2 m/s; opening of chaffer—26 mm, throttle opening—20°; fan speed—1100 r/min; and Crank speed of shale shaker—350 r/min. With these settings, the cleaning loss rate was 1.5% and the impurity rate was 1.9%. In the validation experiment, the average cleaning loss rate was found to be 1.47%, the average impurity rate was 1.96%, and the relative error of the predicted values was 0.03% and 0.06%, respectively. Compared with the cleaning index of combine harvesters with commonly used parameters, the cleaning loss rate was reduced by 0.12% and the impurity rate was reduced by 0.19%. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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13 pages, 1185 KiB

Open AccessArticle

Impact of Combine Harvester Technological Operations on Global Warming Potential

by Dainius Savickas, Dainius Steponavičius, Liudvikas Špokas, Lina Saldukaitė and Michail Semenišin

Appl. Sci. 2021, 11(18), 8662; https://doi.org/10.3390/app11188662 - 17 Sep 2021

Cited by 9 | Viewed by 5521

Abstract

The agricultural machinery is making a considerable negative contribution to the acceleration of global warming. In this study, we analyzed the impact of combine harvesters (CHs) on the global warming potential (GWP) by evaluating the telematics data from 67 CHs operating in Lithuania [...] Read more.

The agricultural machinery is making a considerable negative contribution to the acceleration of global warming. In this study, we analyzed the impact of combine harvesters (CHs) on the global warming potential (GWP) by evaluating the telematics data from 67 CHs operating in Lithuania and Latvia between 2016 and 2020. This study examined the use of their technological operations and the associated impacts on ambient air and performed field tests using the same CH model to determine the composition of exhaust gases and the impact of different technological operations on GWP. The data confirmed the release of significant GWP during indirect operation, and it was estimated that considerable lengths of time were spent in idle (~20%) and transport (~13%) modes. During these operations, over 13% of the total GWP (~27.4 t year⁻¹ per CH), affected by emissions, was released. It was calculated that a GWP reduction exceeding 1 t year⁻¹ per machine can be achieved by optimizing the idling and transport operations. The dual telematics/field test data approach facilitates a comprehensive assessment of both the impact of CH exhaust gases on GWP and the methods for reducing the negative impact on the environment. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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19 pages, 6239 KiB

Open AccessArticle

Design of a Distributed Wireless Sensor Platform for Monitoring and Real-Time Communication of the Environmental Variables during the Supply Chain of Perishable Commodities

by Roque Torres-Sanchez, María Teresa Martínez Zafra, Fulgencio Soto-Valles, Manuel Jiménez-Buendía, Ana Toledo-Moreo and Francisco Artés-Hernández

Appl. Sci. 2021, 11(13), 6183; https://doi.org/10.3390/app11136183 - 3 Jul 2021

Cited by 6 | Viewed by 3866

Abstract

Monitoring the main environmental conditions during storage and transportation of perishable foods is necessary to predict quality losses throughout shelf life. By far, temperature is the main factor affecting quality and shelf life, but there are other variables that would greatly affect quality [...] Read more.

Monitoring the main environmental conditions during storage and transportation of perishable foods is necessary to predict quality losses throughout shelf life. By far, temperature is the main factor affecting quality and shelf life, but there are other variables that would greatly affect quality losses such us relative humidity, O₂, CO₂, ethylene, etc. Thus, the real-time knowledge of the evolution of these parameters during the whole supply chain allows suppliers to prevent for food losses. This paper deeply describes the design of a flexible monitoring system with real-time communication to be used in the supply chain of perishable commodities, using Wi-Fi wireless communication as collaborative networks between different measurement points. Aspects such as consumption, performance and feasibility of the system are described in detail to check the adaptability of its use. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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15 pages, 3256 KiB

Open AccessArticle

Towards In Vivo Monitoring of Ions Accumulation in Trees: Response of an in Planta Organic Electrochemical Transistor Based Sensor to Water Flux Density, Light and Vapor Pressure Deficit Variation

by Davide Amato, Giuseppe Montanaro, Filippo Vurro, Nicola Coppedé, Nunzio Briglia, Angelo Petrozza, Michela Janni, Andrea Zappettini, Francesco Cellini and Vitale Nuzzo

Appl. Sci. 2021, 11(11), 4729; https://doi.org/10.3390/app11114729 - 21 May 2021

Cited by 13 | Viewed by 3994

Abstract

Research on organic electrochemical transistor (OECT) based sensors to monitor in vivo plant traits such as xylem sap concentration is attracting attention for their potential application in precision agriculture. Fabrication and electronic aspects of OECT have been the subject of extensive research while [...] Read more.

Research on organic electrochemical transistor (OECT) based sensors to monitor in vivo plant traits such as xylem sap concentration is attracting attention for their potential application in precision agriculture. Fabrication and electronic aspects of OECT have been the subject of extensive research while its characterization within the plant water relation context deserves further efforts. This study tested the hypothesis that the response (R) of an OECT (bioristor) implanted in the trunk of olive trees is inversely proportional to the water flux density flowing through the plant (Jw). This study also examined the influence on R of vapor pressure deficit (VPD) as coupled/uncoupled with light. R was hourly recorded in potted olive trees for a 10-day period concomitantly with Jw (weight loss method). A subgroup of trees was bagged in order to reduce VPD and in turn Jw, and other trees were located in a walk-in chamber where VPD and light were independently managed. R was tightly sensitive to diurnal oscillation of Jw and at negligible values of Jw (late afternoon and night) R increased. The bioristor was not sensitive to the VPD per se unless a light source was coupled to trigger Jw. This study preliminarily examined the suitability of bioristor to estimate the mean daily nutrients accumulation rate (Ca, K) in leaves comparing chemical and sensor-based procedures showing a good agreement between them opening new perspective towards the application of OECT sensor in precision agricultural cropping systems. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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10 pages, 1399 KiB

Open AccessArticle

Viñamecum: A Computer-Aided Method for Diagnoses of Pests and Diseases in the Vineyard

by Juan Ignacio García-García, Daniel Marín-Aragón, Hanael Maciá and Ana Jiménez-Cantizano

Appl. Sci. 2021, 11(10), 4704; https://doi.org/10.3390/app11104704 - 20 May 2021

Cited by 1 | Viewed by 2797

Abstract

Information and telecommunication technologies (ICTs) offer new opportunities to provide more timely information services to farmers. This work aims to present a progressive web app (PWA) for mobile devices, which incorporates updated technical information on the pests and diseases of grapevines. In its [...] Read more.

Information and telecommunication technologies (ICTs) offer new opportunities to provide more timely information services to farmers. This work aims to present a progressive web app (PWA) for mobile devices, which incorporates updated technical information on the pests and diseases of grapevines. In its development, it generated a database with content related to and photographs of grapevine pests and diseases for access by users using mobile devices. In addition, using an Expert System, the application allows the diagnosis of pathologies and the identification of pests by answering questions that are asked. This PWA is mainly addressed to technicians, students, and winegrowers who want to implement more environmentally friendly crop management strategies. Viñamecum is currently freely. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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20 pages, 8651 KiB

Open AccessArticle

High-Density Wi-Fi Based Sensor Network for Efficient Irrigation Management in Precision Agriculture

by Manuel Jiménez-Buendía, Fulgencio Soto-Valles, Pedro José Blaya-Ros, Ana Toledo-Moreo, Rafael Domingo-Miguel and Roque Torres-Sánchez

Appl. Sci. 2021, 11(4), 1628; https://doi.org/10.3390/app11041628 - 11 Feb 2021

Cited by 14 | Viewed by 4652

Abstract

The application of deficit irrigation techniques is essential in arid or semi-arid areas of the southeast of Spain, where water is a scarce and very costly resource. However, to apply these techniques, it is necessary to carry out preliminary tests on the specific [...] Read more.

The application of deficit irrigation techniques is essential in arid or semi-arid areas of the southeast of Spain, where water is a scarce and very costly resource. However, to apply these techniques, it is necessary to carry out preliminary tests on the specific crop in order to develop the models that allow the optimization of water use while achieving acceptable yields. The system proposed in this article demonstrates the feasibility of using wireless technologies available in most facilities (Wireless Fidelity) to deploy a high-density network of nodes with a variety of heterogeneous sensors to collect data from the soil, plant, and atmosphere. The data are sent and stored in a cloud server for real-time visualization from any mobile device and further analysis. The nodes have been developed using low-cost processors and are equipped with batteries and solar panels, allowing their autonomy to be virtually unlimited, as shown by the consumption studies and tests carried out. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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Review

Jump to: Research, Other

19 pages, 11152 KiB

Open AccessReview

Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming

by Rajat Singh, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Neeraj Priyadarshi and Bhekisipho Twala

Appl. Sci. 2022, 12(24), 12557; https://doi.org/10.3390/app122412557 - 8 Dec 2022

Cited by 31 | Viewed by 8723

Abstract

The United Nations emphasized a significant agenda on reducing hunger and protein malnutrition as well as micronutrient (vitamins and minerals) malnutrition, which is estimated to affect the health of up to two billion people. The UN also recognized this need through Sustainable Development [...] Read more.

The United Nations emphasized a significant agenda on reducing hunger and protein malnutrition as well as micronutrient (vitamins and minerals) malnutrition, which is estimated to affect the health of up to two billion people. The UN also recognized this need through Sustainable Development Goals (SDG 2 and SDG 12) to end hunger and foster sustainable agriculture by enhancing the production and consumption of fruits and vegetables. Previous studies only stressed the various issues in horticulture with regard to industries, but they did not emphasize the centrality of Industry 4.0 technologies for confronting the diverse issues in horticulture, from production to marketing in the context of sustainability. The current study addresses the significance and application of Industry 4.0 technologies such as the Internet of Things, cloud computing, artificial intelligence, blockchain, and big data for horticulture in enhancing traditional practices for disease detection, irrigation management, fertilizer management, maturity identification, marketing, and supply chain, soil fertility, and weather patterns at pre-harvest, harvest, and post-harvest. On the basis of analysis, the article identifies challenges and suggests a few vital recommendations for future work. In horticulture settings, robotics, drones with vision technology and AI for the detection of pests, weeds, plant diseases, and malnutrition, and edge-computing portable devices that can be developed with IoT and AI for predicting and estimating crop diseases are vital recommendations suggested in the study. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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Other

Jump to: Research, Review

11 pages, 275 KiB

Open AccessPerspective

Agriculture 4.0: Is Sub-Saharan Africa Ready?

by Nugun P. Jellason, Elizabeth J. Z. Robinson and Chukwuma C. Ogbaga

Appl. Sci. 2021, 11(12), 5750; https://doi.org/10.3390/app11125750 - 21 Jun 2021

Cited by 55 | Viewed by 9199

Abstract

A fourth agricultural revolution, termed agriculture 4.0, is gradually gaining ground around the globe. It encompasses the application of smart technologies such as artificial intelligence, biotechnology, the internet of things (IoT), big data, and robotics to improve agriculture and the sustainability of food [...] Read more.

A fourth agricultural revolution, termed agriculture 4.0, is gradually gaining ground around the globe. It encompasses the application of smart technologies such as artificial intelligence, biotechnology, the internet of things (IoT), big data, and robotics to improve agriculture and the sustainability of food production. To date, narratives around agriculture 4.0 associated technologies have generally focused on their application in the context of higher-income countries (HICs). In contrast, in this perspective, we critically assess the place of sub-Saharan Africa (SSA) in this new technology trajectory, a region that has received less attention with respect to the application of such technologies. We examine the continent’s readiness based on a number of dimensions such as scale, finance, technology leapfrogging, institutions and governance, education and skills. We critically reviewed the challenges, opportunities, and prospects of adopting agriculture 4.0 technologies in SSA, particularly with regards to how smallholder farmers in the region can be involved through a robust strategy. We find that whilst potential exist for agriculture 4.0 adoption in SSA, there are gaps in knowledge, skills, finance, and infrastructure to ensure successful adoption. Full article

(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)

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Agriculture 4.0 – the Future of Farming Technology

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