Pub Date : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964640
U. Robustelli, Matteo Cutugno, G. Pugliano
The agriculture and food sector will increasingly play a major role in the well-being of humanity. World-scale events, such as wars, climate changes, desertification, pandemic, etc., revealed how fragile humanity is from the point of view of food supply. Therefore, precision farming can provide a remarkable positive contribution to the primary sector globally at various levels. Nowadays, the employment of platforms for product data capture related to farming production and management is extensively available in several fields through local devices. Those systems comprehend sensors, automatic guidance systems with Global Navigation Satellite Systems (GNSSs), and central processing systems. Specifically, GNSS technology plays a central role in the autonomous guidance of tractors and farming robots. Until some years ago, high accuracy was a prerogative of expensive geodetic receivers whereas today high accuracy can be achieved also with low-cost receivers thanks to several factors, among all: the increased availability of GNSS interoperable constellations as well as the accessibility to several augmentation techniques both satellite- and ground-based. These factors are triggering the diffusion of autonomous machinery for farming purposes. This research aims to investigate the performance of a commercial Precise Point Positioning-Real Time Kinematic (PPP-RTK) correction service, employing a low-cost receiver. Two tests have been carried out with two different-grade antennas (a geodetic and a low-cost one). The tests showed that the employment of cost-effective equipment along with the exploitation of correction services allows reaching subdecimetre-level precision in less than one minute when employing a geodetic antenna; accuracy slightly degrades to decimetre-level with the low-cost antenna but the integer ambiguity is resolved in less time. Mean time-to-fix attests to 57 s for test 1 (geodetic antenna) and 30 s for test 2 (low-cost antenna). The times to obtain the first float ambiguity solution are equal to about 15 s for both tests. Integer ambiguity fixed solutions reveal a DRMS of 0.09 m and 0.012 m for test 1 and test 2, respectively. Float solutions reach a DRMS of 0.45 m and 0.63 m for test 1 and test 2, respectively. Lastly, when corrections are not available at all, single point positioning solutions reveal a DRMS of 1.36 m for test 1 and 3.15 m for test 2.
{"title":"A low-cost multi-GNSS PPP-RTK solution for precision agriculture: a preliminary test","authors":"U. Robustelli, Matteo Cutugno, G. Pugliano","doi":"10.1109/MetroAgriFor55389.2022.9964640","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964640","url":null,"abstract":"The agriculture and food sector will increasingly play a major role in the well-being of humanity. World-scale events, such as wars, climate changes, desertification, pandemic, etc., revealed how fragile humanity is from the point of view of food supply. Therefore, precision farming can provide a remarkable positive contribution to the primary sector globally at various levels. Nowadays, the employment of platforms for product data capture related to farming production and management is extensively available in several fields through local devices. Those systems comprehend sensors, automatic guidance systems with Global Navigation Satellite Systems (GNSSs), and central processing systems. Specifically, GNSS technology plays a central role in the autonomous guidance of tractors and farming robots. Until some years ago, high accuracy was a prerogative of expensive geodetic receivers whereas today high accuracy can be achieved also with low-cost receivers thanks to several factors, among all: the increased availability of GNSS interoperable constellations as well as the accessibility to several augmentation techniques both satellite- and ground-based. These factors are triggering the diffusion of autonomous machinery for farming purposes. This research aims to investigate the performance of a commercial Precise Point Positioning-Real Time Kinematic (PPP-RTK) correction service, employing a low-cost receiver. Two tests have been carried out with two different-grade antennas (a geodetic and a low-cost one). The tests showed that the employment of cost-effective equipment along with the exploitation of correction services allows reaching subdecimetre-level precision in less than one minute when employing a geodetic antenna; accuracy slightly degrades to decimetre-level with the low-cost antenna but the integer ambiguity is resolved in less time. Mean time-to-fix attests to 57 s for test 1 (geodetic antenna) and 30 s for test 2 (low-cost antenna). The times to obtain the first float ambiguity solution are equal to about 15 s for both tests. Integer ambiguity fixed solutions reveal a DRMS of 0.09 m and 0.012 m for test 1 and test 2, respectively. Float solutions reach a DRMS of 0.45 m and 0.63 m for test 1 and test 2, respectively. Lastly, when corrections are not available at all, single point positioning solutions reveal a DRMS of 1.36 m for test 1 and 3.15 m for test 2.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132484781","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964541
M. Micheli, S. Pasinetti, M. Lancini, Gabriele Coffetti
Rapid spread of Varroa destructor mite has resulted in high honeybee colony losses. Common monitoring practices are time consuming, manual and operator dependent. This work proposes a camera-based system, integrated inside the beehive, by means of an instrumented honeycomb. The goal is to design a monitoring system for detecting youngest infected bees during the warm seasons and foretic mite throughout the winter, providing the level of infestation inside the hive. Constraints and limits of the designed experimental setup are discussed.
{"title":"Development of a monitoring system to assess honeybee colony health","authors":"M. Micheli, S. Pasinetti, M. Lancini, Gabriele Coffetti","doi":"10.1109/MetroAgriFor55389.2022.9964541","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964541","url":null,"abstract":"Rapid spread of Varroa destructor mite has resulted in high honeybee colony losses. Common monitoring practices are time consuming, manual and operator dependent. This work proposes a camera-based system, integrated inside the beehive, by means of an instrumented honeycomb. The goal is to design a monitoring system for detecting youngest infected bees during the warm seasons and foretic mite throughout the winter, providing the level of infestation inside the hive. Constraints and limits of the designed experimental setup are discussed.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128421204","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9965016
S. Chiappini, V. Giorgi, D. Neri, A. Galli, E. Marcheggiani, Eva Savina Malinverni, R. Pierdicca, M. Balestra
The development of methods and techniques for reconstructing olive tree canopies by point clouds is a long-established topic challenging many researchers. Using Mobile Laser Scanner, we measured single tree parameters in an olive grove in Cartoceto, Italy. The area is a well-known geographical indication agreed by Italian law by Protected Designation of Origin (PDO). According to the agronomic practice, we first estimated the canopy volume using geometrical primitives (paraboloid and toroidal) as ground truth. We have finally compared the canopy values with the volumes obtained by mesh algorithms: Convex hull and Alpha shape, to statistically compare pairwise correlation (Paraboloid - Convex hull $boldsymbol{mathrm{R}^2=0,92}$ and Toroid - Alpha shape $boldsymbol{mathrm{R}^2=0,91)}$. This preliminary analysis provides a theoretical benchmark for future implementations, such as the possibility of including LiDAR (Light Detection and Ranging) in the mechanized pruning process.
{"title":"Innovation in olive-growing by Proximal sensing LiDAR for tree volume estimation","authors":"S. Chiappini, V. Giorgi, D. Neri, A. Galli, E. Marcheggiani, Eva Savina Malinverni, R. Pierdicca, M. Balestra","doi":"10.1109/MetroAgriFor55389.2022.9965016","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9965016","url":null,"abstract":"The development of methods and techniques for reconstructing olive tree canopies by point clouds is a long-established topic challenging many researchers. Using Mobile Laser Scanner, we measured single tree parameters in an olive grove in Cartoceto, Italy. The area is a well-known geographical indication agreed by Italian law by Protected Designation of Origin (PDO). According to the agronomic practice, we first estimated the canopy volume using geometrical primitives (paraboloid and toroidal) as ground truth. We have finally compared the canopy values with the volumes obtained by mesh algorithms: Convex hull and Alpha shape, to statistically compare pairwise correlation (Paraboloid - Convex hull $boldsymbol{mathrm{R}^2=0,92}$ and Toroid - Alpha shape $boldsymbol{mathrm{R}^2=0,91)}$. This preliminary analysis provides a theoretical benchmark for future implementations, such as the possibility of including LiDAR (Light Detection and Ranging) in the mechanized pruning process.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126648030","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964928
F. Zottele, Paolo Crocetta, V. Baiocchi
The recent deployment of GNSS-RTK positioning on remotely piloted vehicles has increased real-time positioning accuracy by almost three orders of magnitude. This not only provides a significant geometric improvement but, in practice, makes possible some applications that were simply not possible before. For example, positioning crops with centimetre accuracy makes it possible to distinguish a single plant and to detect or treat that very plant without any possibility of misunderstanding. This is obviously not possible with ‘traditional’ drones that work in ‘point positioning’ with indeterminacies of even tens of metres. In this paper we will illustrate how the possibilities of RTK can be applied to a specific vinepathology. The symptoms of the flavescence dorée and bois noir are grouped into the so-called Grapevine Yellows (GY). These diseases are affecting the viticultural regions worldwide and all varieties and rootstocks seem susceptible but with varying degrees of severity. Typical symptoms include discolouration and necrosis of leaf veins and leaf blades, downward curling of leaves, lack or incomplete lignification of shoots, stunting and necrosis of shoots, abortion of inflorescences and shrivelling of berries. The compulsory control plan for the fight of these diseases includes both the use of insecticides and the eradication of the vines. This latter is part of a monitoring plan of the grapevine yellows that aims to identify outbreaks of the disease and its progression and limit the compulsory phytosanitary control only in the truly affected areas. The identification of the GY is a very time-consuming technical work because each vineyard must be visually inspected plant by plant. This type of monitoring is made even more difficult in the case of steeply sloping vineyards and where the vineyard landscape is fragmented. So, we raised the following question: is it possible to use Unmanned Aerial Vehicles (UAVs or drones) to remotely monitor the vines that are difficult to reach and identify the grapevine yellows? We present here the results of our field tests made in Trentino (IT) with different drone models (prosumer and professional) and with different types of image acquisition sensors (RGB and multi-spectral).
{"title":"How important is UAVs RTK accuracy for the identification of certain vine diseases?","authors":"F. Zottele, Paolo Crocetta, V. Baiocchi","doi":"10.1109/MetroAgriFor55389.2022.9964928","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964928","url":null,"abstract":"The recent deployment of GNSS-RTK positioning on remotely piloted vehicles has increased real-time positioning accuracy by almost three orders of magnitude. This not only provides a significant geometric improvement but, in practice, makes possible some applications that were simply not possible before. For example, positioning crops with centimetre accuracy makes it possible to distinguish a single plant and to detect or treat that very plant without any possibility of misunderstanding. This is obviously not possible with ‘traditional’ drones that work in ‘point positioning’ with indeterminacies of even tens of metres. In this paper we will illustrate how the possibilities of RTK can be applied to a specific vinepathology. The symptoms of the flavescence dorée and bois noir are grouped into the so-called Grapevine Yellows (GY). These diseases are affecting the viticultural regions worldwide and all varieties and rootstocks seem susceptible but with varying degrees of severity. Typical symptoms include discolouration and necrosis of leaf veins and leaf blades, downward curling of leaves, lack or incomplete lignification of shoots, stunting and necrosis of shoots, abortion of inflorescences and shrivelling of berries. The compulsory control plan for the fight of these diseases includes both the use of insecticides and the eradication of the vines. This latter is part of a monitoring plan of the grapevine yellows that aims to identify outbreaks of the disease and its progression and limit the compulsory phytosanitary control only in the truly affected areas. The identification of the GY is a very time-consuming technical work because each vineyard must be visually inspected plant by plant. This type of monitoring is made even more difficult in the case of steeply sloping vineyards and where the vineyard landscape is fragmented. So, we raised the following question: is it possible to use Unmanned Aerial Vehicles (UAVs or drones) to remotely monitor the vines that are difficult to reach and identify the grapevine yellows? We present here the results of our field tests made in Trentino (IT) with different drone models (prosumer and professional) and with different types of image acquisition sensors (RGB and multi-spectral).","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127011637","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9965119
F. Carollo, V. Ferro, V. Palmeri, V. Pampalone, A. Nicosia
Soil erosion induced by rainfall is mainly due to the rainfall impact besides the consequent surface runoff. Rainfall kinetic energy is the most used variable to represent its erosivity. The latter represents the weathering attitude to erode soil and is a fundamental variable of the erosion process. Consequently, precise measurements of rainfall erosivity have to perform to develop a reliable prediction model of the erosive phenomenon. Currently, impact energy can be reliably measured only by disdrometers. These instruments measure the Drop Size Distribution (DSD) which, joined with the raindrop falling velocity, allow to calculate, by integration, the impact kinetic energy. However, disdrometers are expensive tools that imply to collect and process a remarkable amount of data, and for these reasons, they are not suitable for land large scale use. Without direct measurements, the rainfall impact energy is currently estimated using only the rainfall intensity, widely detected by the recording rain-gauge network operating all over the country. Recently, an innovative method to measure the rainfall energy, subject of a patent, has been proposed. This method is based on the simultaneous detection, in a given time interval, of the rainfall intensity and the number of raindrops that hit a specific surface. In this paper a theoretical analysis aimed at improving the reliability of this rainfall energy measurement is firstly presented. The developed analysis accounts for the detection of a further variable deriving from the momentum distribution. Then, the reliability of the proposed approach was tested using 44,695 DSDs recorded in Palermo in the period 2006–2014. Using the proposed approach, the reliability of the rainfall energy measurement can significantly improve.
{"title":"Theoretical advancements on a recently proposed method to measure rainfall energy","authors":"F. Carollo, V. Ferro, V. Palmeri, V. Pampalone, A. Nicosia","doi":"10.1109/MetroAgriFor55389.2022.9965119","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9965119","url":null,"abstract":"Soil erosion induced by rainfall is mainly due to the rainfall impact besides the consequent surface runoff. Rainfall kinetic energy is the most used variable to represent its erosivity. The latter represents the weathering attitude to erode soil and is a fundamental variable of the erosion process. Consequently, precise measurements of rainfall erosivity have to perform to develop a reliable prediction model of the erosive phenomenon. Currently, impact energy can be reliably measured only by disdrometers. These instruments measure the Drop Size Distribution (DSD) which, joined with the raindrop falling velocity, allow to calculate, by integration, the impact kinetic energy. However, disdrometers are expensive tools that imply to collect and process a remarkable amount of data, and for these reasons, they are not suitable for land large scale use. Without direct measurements, the rainfall impact energy is currently estimated using only the rainfall intensity, widely detected by the recording rain-gauge network operating all over the country. Recently, an innovative method to measure the rainfall energy, subject of a patent, has been proposed. This method is based on the simultaneous detection, in a given time interval, of the rainfall intensity and the number of raindrops that hit a specific surface. In this paper a theoretical analysis aimed at improving the reliability of this rainfall energy measurement is firstly presented. The developed analysis accounts for the detection of a further variable deriving from the momentum distribution. Then, the reliability of the proposed approach was tested using 44,695 DSDs recorded in Palermo in the period 2006–2014. Using the proposed approach, the reliability of the rainfall energy measurement can significantly improve.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537774","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964687
Massimiliano Proietti, A. Marini, A. Garinei, Gianluca Rossi, Federico Bianchi, M. Marconi, Silvia Discepolo, M. Martarelli, Maria Teresa Calcagni, Giacomo Zeni, P. Castellini, Stefano Speziali
Black Soldier flies (BSFs) are very effective for the treatment of organic waste and their transformation into insect proteins and oils that can be used to produce feed and biofuels. An increasing number of startups and companies are breeding BSFs to take advantage of the numerous potential applications due to the larval diets. Although the breeding of BSF larvae requires artificially controlled conditions, methods for the characterization of the life cycle in production plan are lacking. Most of the analyses and procedures available in the literature cannot be used within the production lines of breeders. In the present study, an exploration of non-contact measurements (RGB video, thermal, Hyperspectral imaging) and of the analysis methodologies was carried out in order to identify the ones which are most significant for the different phases of the BSF life cycle, and which can be automated within the production lines. The result of the study was the definition of the criteria for the characterization, through non-contact measurements of the life cycle of the BSFs: computer vision algorithms based on image and data acquisitions were developed using 1) RGB camera for size / weight estimation and movement / vitality for the phases where the nutritional substrate is not present (pupae); 2) IR camera for the evaluation of movement / vitality for the phases where the nutrient substrate (larvae) is present and for the identification of temperature anomalies (metabolism too slow or too fast); 3) hyperspectral chamber to evaluate the growth of the larvae in relation to the chosen diet.
{"title":"Non-invasive measurements for characterization of Hermetia Illucens (BSF) life cycle in rearing plant","authors":"Massimiliano Proietti, A. Marini, A. Garinei, Gianluca Rossi, Federico Bianchi, M. Marconi, Silvia Discepolo, M. Martarelli, Maria Teresa Calcagni, Giacomo Zeni, P. Castellini, Stefano Speziali","doi":"10.1109/MetroAgriFor55389.2022.9964687","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964687","url":null,"abstract":"Black Soldier flies (BSFs) are very effective for the treatment of organic waste and their transformation into insect proteins and oils that can be used to produce feed and biofuels. An increasing number of startups and companies are breeding BSFs to take advantage of the numerous potential applications due to the larval diets. Although the breeding of BSF larvae requires artificially controlled conditions, methods for the characterization of the life cycle in production plan are lacking. Most of the analyses and procedures available in the literature cannot be used within the production lines of breeders. In the present study, an exploration of non-contact measurements (RGB video, thermal, Hyperspectral imaging) and of the analysis methodologies was carried out in order to identify the ones which are most significant for the different phases of the BSF life cycle, and which can be automated within the production lines. The result of the study was the definition of the criteria for the characterization, through non-contact measurements of the life cycle of the BSFs: computer vision algorithms based on image and data acquisitions were developed using 1) RGB camera for size / weight estimation and movement / vitality for the phases where the nutritional substrate is not present (pupae); 2) IR camera for the evaluation of movement / vitality for the phases where the nutrient substrate (larvae) is present and for the identification of temperature anomalies (metabolism too slow or too fast); 3) hyperspectral chamber to evaluate the growth of the larvae in relation to the chosen diet.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125709971","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964768
D. Buonocore, M. Carratù, G. Ciavolino, D. Di Caro, C. Liguori, A. Pietrosanto
In recent years, emerging technologies have allowed the measurement, analysis, and control of many processes. Many researchers have taken particular attention to developing smart sensing solutions for different fields, such as Smart Industries, Smart Metering, Smart Building, and so on. However, this new enabling and emerging technologies can also be used to analyze and improve craft operation processes in agriculture. An example is the natural fig drying process, usually made adopting only natural sources. In this case, the process is regulated by the availability of unpredictable climate conditions, so the correct detection of the drying process ending point represents an essential aspect of improving the drying process throughput. The paper aims to describe the design of a smart sensing system made for monitoring the fig drying process, measuring different parameters such as the drying temperature and relative humidity and correlating them with the weight loss and water activity of figs. The analysis of data collected during the drying process has been reported and discussed to demonstrate its effectiveness in speeding up the drying process.
{"title":"A smart sensing system for the fig drying process","authors":"D. Buonocore, M. Carratù, G. Ciavolino, D. Di Caro, C. Liguori, A. Pietrosanto","doi":"10.1109/MetroAgriFor55389.2022.9964768","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964768","url":null,"abstract":"In recent years, emerging technologies have allowed the measurement, analysis, and control of many processes. Many researchers have taken particular attention to developing smart sensing solutions for different fields, such as Smart Industries, Smart Metering, Smart Building, and so on. However, this new enabling and emerging technologies can also be used to analyze and improve craft operation processes in agriculture. An example is the natural fig drying process, usually made adopting only natural sources. In this case, the process is regulated by the availability of unpredictable climate conditions, so the correct detection of the drying process ending point represents an essential aspect of improving the drying process throughput. The paper aims to describe the design of a smart sensing system made for monitoring the fig drying process, measuring different parameters such as the drying temperature and relative humidity and correlating them with the weight loss and water activity of figs. The analysis of data collected during the drying process has been reported and discussed to demonstrate its effectiveness in speeding up the drying process.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131175124","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964699
Lukas Meyer, J. Gedschold, Tim Erich Wegner, G. del Galdo, A. Kalisz
Digital field recordings are central to most precision agriculture systems since they can replicate the physical environment and thus monitor the state of an entire field or individual plants. Using different sensors, such as cameras and radar, data can be collected from various domains. Through the combination of radio wave propagation and visible light phenomena, it is possible to enhance, e.g., the optical condition of a fruit with internal parameters such as the water content. This paper proposes a method to correct sensor errors to perform data fusion. As an example, we observe a watermelon with camera and radar sensors and present a system architecture for the visualization of both sensors. For this purpose, we constructed a handheld platform on which both sensors are mounted. In our report, the radar is analyzed in terms of systematic and stochastic errors to formulate an angle-dependent mapping function for error correction. It is successfully shown that camera and radar data are correctly assigned with a watermelon used as a target object, demonstrated by a 3D reconstruction. The proposed system shows promising results for sensor overlay, but radar data remain challenging to interpret.
{"title":"Enhancement of Vision-Based 3D Reconstruction Systems Using Radar for Smart Farming","authors":"Lukas Meyer, J. Gedschold, Tim Erich Wegner, G. del Galdo, A. Kalisz","doi":"10.1109/MetroAgriFor55389.2022.9964699","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964699","url":null,"abstract":"Digital field recordings are central to most precision agriculture systems since they can replicate the physical environment and thus monitor the state of an entire field or individual plants. Using different sensors, such as cameras and radar, data can be collected from various domains. Through the combination of radio wave propagation and visible light phenomena, it is possible to enhance, e.g., the optical condition of a fruit with internal parameters such as the water content. This paper proposes a method to correct sensor errors to perform data fusion. As an example, we observe a watermelon with camera and radar sensors and present a system architecture for the visualization of both sensors. For this purpose, we constructed a handheld platform on which both sensors are mounted. In our report, the radar is analyzed in terms of systematic and stochastic errors to formulate an angle-dependent mapping function for error correction. It is successfully shown that camera and radar data are correctly assigned with a watermelon used as a target object, demonstrated by a 3D reconstruction. The proposed system shows promising results for sensor overlay, but radar data remain challenging to interpret.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"14 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130199151","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9964822
L. Vergni, A. Parisi, F. Todisco
The NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) product is one of the few gridded precipitation products available at a sub-hourly temporal scale. This high temporal resolution is required in many hydrological applications, including the estimation of the rainfall-runoff erosivity factor R, used in the USLE and RUSLE soil erosion prediction models. In this study, through a comparison with data measured at two rain gauges in central Italy, the limits and potential of the GPM-IMERG product on O.t-degree resolution in providing estimates of the R-factor are highlighted. Results indicate that the product can provide accurate estimates in areas relatively homogeneous from an orographic and climatic point of view. On the other hand, it fails to identify the gradients of both precipitation and erosivity that characterize more complex areas, such as those close to the mountain ranges.
{"title":"Evaluation of the GPM IMERG half-hourly final precipitation product in the quantification of rainfall erosivity in central Italy","authors":"L. Vergni, A. Parisi, F. Todisco","doi":"10.1109/MetroAgriFor55389.2022.9964822","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964822","url":null,"abstract":"The NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) product is one of the few gridded precipitation products available at a sub-hourly temporal scale. This high temporal resolution is required in many hydrological applications, including the estimation of the rainfall-runoff erosivity factor R, used in the USLE and RUSLE soil erosion prediction models. In this study, through a comparison with data measured at two rain gauges in central Italy, the limits and potential of the GPM-IMERG product on O.t-degree resolution in providing estimates of the R-factor are highlighted. Results indicate that the product can provide accurate estimates in areas relatively homogeneous from an orographic and climatic point of view. On the other hand, it fails to identify the gradients of both precipitation and erosivity that characterize more complex areas, such as those close to the mountain ranges.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128212422","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 : 2022-11-03DOI: 10.1109/MetroAgriFor55389.2022.9965083
M. Ceccarelli, A. Barbaresi, Giulia Menichetti, Enrica Santolini, Marco Bovo, P. Tassinari, Francesco Barreca, D. Torreggiani
A fast and reliable estimation of building energy need is essential in agricultural building design, nonetheless, a large number of simulations is required to obtain better energy saving solutions. The aim of this work is to understand if machine learning can substitute numerical simulations and speed up the building design process and assess the incidence of specific architectural elements. Supervised regression models has been trained and tested in a data-set of thousands simulations performed on a case-study agricultural building. Among the algorithms, the tree-based Extreme Gradient Boosting showed the best performance. A study on model explainability has been carried out using SHAP and features importance, which is fundamental to help academics and professionals devise better design strategies for both new constructions and retrofitting interventions.
{"title":"Simulations in agricultural buildings: a machine learning approach to forecast seasonal energy need","authors":"M. Ceccarelli, A. Barbaresi, Giulia Menichetti, Enrica Santolini, Marco Bovo, P. Tassinari, Francesco Barreca, D. Torreggiani","doi":"10.1109/MetroAgriFor55389.2022.9965083","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9965083","url":null,"abstract":"A fast and reliable estimation of building energy need is essential in agricultural building design, nonetheless, a large number of simulations is required to obtain better energy saving solutions. The aim of this work is to understand if machine learning can substitute numerical simulations and speed up the building design process and assess the incidence of specific architectural elements. Supervised regression models has been trained and tested in a data-set of thousands simulations performed on a case-study agricultural building. Among the algorithms, the tree-based Extreme Gradient Boosting showed the best performance. A study on model explainability has been carried out using SHAP and features importance, which is fundamental to help academics and professionals devise better design strategies for both new constructions and retrofitting interventions.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128217389","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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