The assessment of pesticide deposition is of key importance for the prevention of off-target area contamination, as well as to ensure the efficiency of the pesticide application. It is required by regulatory authorities also to quantify the drift potential of every possible sprayer configuration. As a matter of fact, the standard methodologies to compare sprayers' functional performance require large amounts of time, and the results are not always repeatable, due to the multitude of uncontrollable variables. This study proposes and tests an innovative approach in a laboratory wind tunnel based on resistive-based measurements applied to fluorescent tracers to address this challenge effectively. Our method utilizes screen-printed electrodes integrated into the material collector for measurement of the deposited material in real time shortly after the spray application. The estimation of the material by the standard optical method was also done along with the resistive-based method and compared with the measured weight used as a benchmark reference. Our experimental results demonstrated that both the optical and the resistive-based methods overestimated the amount of deposited material compared to weight measurement, but the overall estimation error remained below $text{2.5} ,text{g}$. The measurements also showed that 90% of material deposition occurred at approximately $text{11.5} ,text{m}$, providing valuable insights into the spatial distribution of sprayed materials. This real-time assessment leveraging resistive measurement techniques offers substantial benefits for laboratory testing of spraying machines and has also the potential for in-field resource management and monitoring. Despite the promising potential for real-time estimation of spray drift deposition, further research and testing are required to improve the method.
{"title":"Advancements in the Development of Resistive-Based Method Applied to Optical Tracers for Real-Time Estimation of Spray Drift Deposition","authors":"Ayesha Ali;Antonio Altana;Lorenzo Becce;Saba Amin;Paolo Lugli;Luisa Petti;Fabrizio Mazzetto","doi":"10.1109/TAFE.2024.3474179","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3474179","url":null,"abstract":"The assessment of pesticide deposition is of key importance for the prevention of off-target area contamination, as well as to ensure the efficiency of the pesticide application. It is required by regulatory authorities also to quantify the drift potential of every possible sprayer configuration. As a matter of fact, the standard methodologies to compare sprayers' functional performance require large amounts of time, and the results are not always repeatable, due to the multitude of uncontrollable variables. This study proposes and tests an innovative approach in a laboratory wind tunnel based on resistive-based measurements applied to fluorescent tracers to address this challenge effectively. Our method utilizes screen-printed electrodes integrated into the material collector for measurement of the deposited material in real time shortly after the spray application. The estimation of the material by the standard optical method was also done along with the resistive-based method and compared with the measured weight used as a benchmark reference. Our experimental results demonstrated that both the optical and the resistive-based methods overestimated the amount of deposited material compared to weight measurement, but the overall estimation error remained below <inline-formula><tex-math>$text{2.5} ,text{g}$</tex-math></inline-formula>. The measurements also showed that 90% of material deposition occurred at approximately <inline-formula><tex-math>$text{11.5} ,text{m}$</tex-math></inline-formula>, providing valuable insights into the spatial distribution of sprayed materials. This real-time assessment leveraging resistive measurement techniques offers substantial benefits for laboratory testing of spraying machines and has also the potential for in-field resource management and monitoring. Despite the promising potential for real-time estimation of spray drift deposition, further research and testing are required to improve the method.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"18-25"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10723093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TAFE.2024.3466561
Asish Bera;Ondrej Krejcar;Debotosh Bhattacharjee
Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively; implying RAFA-Net's generalization capability.
{"title":"RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition","authors":"Asish Bera;Ondrej Krejcar;Debotosh Bhattacharjee","doi":"10.1109/TAFE.2024.3466561","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3466561","url":null,"abstract":"Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively; implying RAFA-Net's generalization capability.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"121-133"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TAFE.2024.3466552
Flávio Bastos Campos;Torben Oliver Callesen;Giorgio Alberti;Leonardo Montagnani;Massimo Tagliavini;Damiano Zanotelli
Detailed monitoring of agroecosystem water vapor losses is essential for improving water management schemes. In this study, a combination of eddy covariance and sap flow sensors was used to examine the responses of evapotranspirative components and water use efficiency of a grassed vineyard to meteorological drivers during a dry spell. Results showed that the grapevines dominated the ecosystem fluxes of carbon and water in the mornings, after which they closed their stomata to limit transpiration. The grasses continued transpiring throughout the day, decreasing overall water use efficiency of the vineyard. Our findings emphasize the importance of short-timescale response monitoring in understanding vineyard water fluxes.
{"title":"Meteorological Drivers of Vineyard Water Vapor Loss and Water Use Efficiency During Dry Days","authors":"Flávio Bastos Campos;Torben Oliver Callesen;Giorgio Alberti;Leonardo Montagnani;Massimo Tagliavini;Damiano Zanotelli","doi":"10.1109/TAFE.2024.3466552","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3466552","url":null,"abstract":"Detailed monitoring of agroecosystem water vapor losses is essential for improving water management schemes. In this study, a combination of eddy covariance and sap flow sensors was used to examine the responses of evapotranspirative components and water use efficiency of a grassed vineyard to meteorological drivers during a dry spell. Results showed that the grapevines dominated the ecosystem fluxes of carbon and water in the mornings, after which they closed their stomata to limit transpiration. The grasses continued transpiring throughout the day, decreasing overall water use efficiency of the vineyard. Our findings emphasize the importance of short-timescale response monitoring in understanding vineyard water fluxes.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"49-55"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TAFE.2024.3469538
Lennart Almstedt;Francesco Betti Sorbelli;Bas Boom;Rosalba Calvini;Elena Costi;Alexandru Dinca;Veronica Ferrari;Daniele Giannetti;Loretta Ichim;Amin Kargar;Catalin Lazar;Lara Maistrello;Alfredo Navarra;David Niederprüm;Peter Offermans;Brendan O'Flynn;Lorenzo Palazzetti;Niccolò Patelli;Cristina M. Pinotti;Dan Popescu;Aravind K. Rangarajan;Liviu Serghei;Alessandro Ulrici;Lars Wolf;Dimitrios Zorbas;Leonard Zurek
The invasive insect brown marmorated stink bug (BMSB) is an emerging pest of global importance, as it is destroying fruits and seeds, having caused estimated damages of € 588 million to crops in 2019 in Northern Italy alone. An open challenge is to improve monitoring of BMSB in order to be able to deploy countermeasures more efficiently and to increase consumer confidence in the end product. The Horizon 2020 Haly.ID project seeks to reduce or eliminate dependence on conventional monitoring tools and practices, such as traps, baits, visual inspections, sweep netting, and tree beating. In their place, the project proposes the use of unmanned aerial vehicle (UAV) and Internet of Things (IoT) solutions for monitoring the insect population and investigates novel methods for enhancing the quality of fruit in the market. In this work, we focus on the novel autonomous IoT insect monitoring system consisting of multiple innovative solutions for BMSB monitoring and trusted data management developed in Haly.ID. In particular, this article describes the challenges faced when integrating and deploying this monitoring system consisting of those different parts and aims at presenting valuable “lessons learned” for the realization of future deployments. We show that massive over-provisioning of power supply and network speed allows to adapt the system at run-time reflecting changing project requirements, and to conduct experiments remotely. At the same time, over-provisioning introduces new weak points impacting the system reliability, such as cables that can be unplugged or damaged.
{"title":"A Comprehensive Pest Monitoring System for Brown Marmorated Stink Bug","authors":"Lennart Almstedt;Francesco Betti Sorbelli;Bas Boom;Rosalba Calvini;Elena Costi;Alexandru Dinca;Veronica Ferrari;Daniele Giannetti;Loretta Ichim;Amin Kargar;Catalin Lazar;Lara Maistrello;Alfredo Navarra;David Niederprüm;Peter Offermans;Brendan O'Flynn;Lorenzo Palazzetti;Niccolò Patelli;Cristina M. Pinotti;Dan Popescu;Aravind K. Rangarajan;Liviu Serghei;Alessandro Ulrici;Lars Wolf;Dimitrios Zorbas;Leonard Zurek","doi":"10.1109/TAFE.2024.3469538","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3469538","url":null,"abstract":"The invasive insect brown marmorated stink bug (BMSB) is an emerging pest of global importance, as it is destroying fruits and seeds, having caused estimated damages of € 588 million to crops in 2019 in Northern Italy alone. An open challenge is to improve monitoring of BMSB in order to be able to deploy countermeasures more efficiently and to increase consumer confidence in the end product. The Horizon 2020 <sc>Haly.ID</small> project seeks to reduce or eliminate dependence on conventional monitoring tools and practices, such as traps, baits, visual inspections, sweep netting, and tree beating. In their place, the project proposes the use of unmanned aerial vehicle (UAV) and Internet of Things (IoT) solutions for monitoring the insect population and investigates novel methods for enhancing the quality of fruit in the market. In this work, we focus on the novel autonomous IoT insect monitoring system consisting of multiple innovative solutions for BMSB monitoring and trusted data management developed in <sc>Haly.ID</small>. In particular, this article describes the challenges faced when integrating and deploying this monitoring system consisting of those different parts and aims at presenting valuable “lessons learned” for the realization of future deployments. We show that massive over-provisioning of power supply and network speed allows to adapt the system at run-time reflecting changing project requirements, and to conduct experiments remotely. At the same time, over-provisioning introduces new weak points impacting the system reliability, such as cables that can be unplugged or damaged.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"110-120"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1109/TAFE.2024.3472304
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TAFE.2024.3472304","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3472304","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1109/TAFE.2024.3472308
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TAFE.2024.3472308","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3472308","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1109/TAFE.2024.3468408
Francois Rivet;Matías Miguez
The global food and agriculture industry is rapidly evolving, driven by advances in electronic technologies and data-driven methodologies. These innovations are critical to addressing the pressing challenges of food security, sustainable farming, and precision agriculture. The first edition of the IEEE Conference on AgriFood Electronics (CAFE 2023) was held in Torino, Italy. It highlighted the groundbreaking research in these areas, bringing together experts from academia and industry to discuss the latest technological advancements in agrifood electronics.
{"title":"Guest Editorial Special Issue on IEEE Conference on AgriFood Electronics (CAFE 2023)","authors":"Francois Rivet;Matías Miguez","doi":"10.1109/TAFE.2024.3468408","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3468408","url":null,"abstract":"The global food and agriculture industry is rapidly evolving, driven by advances in electronic technologies and data-driven methodologies. These innovations are critical to addressing the pressing challenges of food security, sustainable farming, and precision agriculture. The first edition of the IEEE Conference on AgriFood Electronics (CAFE 2023) was held in Torino, Italy. It highlighted the groundbreaking research in these areas, bringing together experts from academia and industry to discuss the latest technological advancements in agrifood electronics.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"168-169"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we investigate an intelligent ground penetrating radar (GPR) that facilitates root-zone soil moisture estimation, a key parameter in precision agriculture. To create an intelligent GPR, we must train machine learning (ML) methods applied to the GPR-received signal. This process requires a large number of labeled GPR data that would be time-consuming and labor-intensive if created via field measurements. This article uses gprMAX software to emulate drone-coupled GPR received signal to generate large-scale data for training ML models. The data are created via a 1.5 GHz Ricker waveform considering a three-layer soil consistent with a realistic soil horizon model. The approach is structured as follows: first, we generate a synthetic dataset using gprMAX. Feature engineering techniques are then employed to extract meaningful components from the GPR signals, followed by a rigorous selection process to identify the most effective ML model for soil moisture prediction. Finally, we validate our model by integrating synthetic data with real GPR data collected at the SoilX lab at Worcester Polytechnic Institute, enhancing prediction accuracy and generalization capability. Our proposed model achieves an overall average root-mean-squared error of 0.5%, and 1.56 cm for moisture and depth estimations, respectively. The proposed intelligent GPR, when installed on a drone, enables high horizontal (e.g., 10 m) and vertical (e.g., 1.5 cm) resolution and high penetration depth (beyond 2 m) megafarm root-zone 3-D moisture map creation. Thus, it offers much higher capabilities when compared to traditional methods, such as synthetic aperture radar and satellite imaging. These results facilitate efficient farming practices, such as optimizing irrigation models, for better crop yields and food security.
{"title":"Advancing Precision Agriculture: Machine Learning-Enhanced GPR Analysis for Root-Zone Soil Moisture Assessment in Mega Farms","authors":"Himan Namdari;Majid Moradikia;Seyed Zekavat;Radwin Askari;Oren Mangoubi;Doug Petkie","doi":"10.1109/TAFE.2024.3455238","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3455238","url":null,"abstract":"In this article, we investigate an intelligent ground penetrating radar (GPR) that facilitates root-zone soil moisture estimation, a key parameter in precision agriculture. To create an intelligent GPR, we must train machine learning (ML) methods applied to the GPR-received signal. This process requires a large number of labeled GPR data that would be time-consuming and labor-intensive if created via field measurements. This article uses gprMAX software to emulate <italic>drone-coupled GPR</i> received signal to generate large-scale data for training ML models. The data are created via a 1.5 GHz Ricker waveform considering a three-layer soil consistent with a realistic soil horizon model. The approach is structured as follows: first, we generate a synthetic dataset using gprMAX. Feature engineering techniques are then employed to extract meaningful components from the GPR signals, followed by a rigorous selection process to identify the most effective ML model for soil moisture prediction. Finally, we validate our model by integrating synthetic data with real GPR data collected at the <italic>SoilX</i> lab at Worcester Polytechnic Institute, enhancing prediction accuracy and generalization capability. Our proposed model achieves an overall average root-mean-squared error of 0.5%, and 1.56 cm for moisture and depth estimations, respectively. The proposed intelligent GPR, when installed on a drone, enables high horizontal (e.g., 10 m) and vertical (e.g., 1.5 cm) resolution and high penetration depth (beyond 2 m) megafarm root-zone 3-D moisture map creation. Thus, it offers much higher capabilities when compared to traditional methods, such as synthetic aperture radar and satellite imaging. These results facilitate efficient farming practices, such as optimizing irrigation models, for better crop yields and food security.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"98-109"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents an in-depth exploration of multifaceted efforts in agricultural research aimed at addressing the unpredictable nature of crop production and related processes, including the demonstration of data collection and its application. This research focuses on leveraging current technologies and devising sustainable solutions to mitigate uncertainties attributed to natural climatic conditions and infectious agents. The central theme of this review centers around the utilization of Internet of things sensors for data collection, cloud software for data processing, and the integration of diverse machine learning algorithms for data analysis. The objective is to advance insights into the application of these technologies in agriculture and their potential to enhance crop yield and sustainability. The article comprehensively explores the technological landscape and the levels at which current research is being conducted, shedding light on machine learning algorithms, their specific functions, and comparative analysis of each algorithm based on different use cases. Furthermore, the article presents an implementation approach that integrates sensors and machine learning. Its primary application is to predict the type of crop to produce based on nutrient levels detected by the sensors.
{"title":"Enabling Data Collection and Analysis for Precision Agriculture in Smart Farms","authors":"Akhilesh Kumar Singh;Fru Ngwa Fru Junior;Ngu Leonel Mainsah;Bande Abdoul-Rahmane","doi":"10.1109/TAFE.2024.3454644","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3454644","url":null,"abstract":"This article presents an in-depth exploration of multifaceted efforts in agricultural research aimed at addressing the unpredictable nature of crop production and related processes, including the demonstration of data collection and its application. This research focuses on leveraging current technologies and devising sustainable solutions to mitigate uncertainties attributed to natural climatic conditions and infectious agents. The central theme of this review centers around the utilization of Internet of things sensors for data collection, cloud software for data processing, and the integration of diverse machine learning algorithms for data analysis. The objective is to advance insights into the application of these technologies in agriculture and their potential to enhance crop yield and sustainability. The article comprehensively explores the technological landscape and the levels at which current research is being conducted, shedding light on machine learning algorithms, their specific functions, and comparative analysis of each algorithm based on different use cases. Furthermore, the article presents an implementation approach that integrates sensors and machine learning. Its primary application is to predict the type of crop to produce based on nutrient levels detected by the sensors.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"3 1","pages":"69-85"},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Water is a not-so-renewable resource. Agriculture is impacting for more than 70% of fresh water use worldwide. Considering the increase of population it is fundamental to act in order to reduce water usage. The WAPPFRUIT project aims to design an automatic irrigation system, based on data of water availability in the soil gathered directly in the orchards. Matric potential data are used to determine the exact water demand of the trees, thanks to specific thresholds adapted to the actual soil and crop type. Furthermore, an electronic system based on simple, small, and ultra-low-power devices works together an automatic algorithm to manage the watering events. We tested this approach in three orchards in north-west Italy, comparing our approach to the one used by the farmers. The results show an average water saving of nearly 50% keeping the fruit production comparable to the reference solution. This approach is a clear example of how electronics and technology can really impact agriculture and food production.
{"title":"WAPPFRUIT—An Automatic System for Drip Irrigation in Orchards Based on Real-Time Soil Matric Potential Data","authors":"Mattia Barezzi;Alessandro Sanginario;Davide Canone;Davide Gisolo;Alessio Gentile;Luca Nari;Francesca Pettiti;Umberto Garlando","doi":"10.1109/TAFE.2024.3455171","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3455171","url":null,"abstract":"Water is a not-so-renewable resource. Agriculture is impacting for more than 70% of fresh water use worldwide. Considering the increase of population it is fundamental to act in order to reduce water usage. The WAPPFRUIT project aims to design an automatic irrigation system, based on data of water availability in the soil gathered directly in the orchards. Matric potential data are used to determine the exact water demand of the trees, thanks to specific thresholds adapted to the actual soil and crop type. Furthermore, an electronic system based on simple, small, and ultra-low-power devices works together an automatic algorithm to manage the watering events. We tested this approach in three orchards in north-west Italy, comparing our approach to the one used by the farmers. The results show an average water saving of nearly 50% keeping the fruit production comparable to the reference solution. This approach is a clear example of how electronics and technology can really impact agriculture and food production.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"293-305"},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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