Research

19 pages, 4904 KiB

Open AccessArticle

Identifying Queenlessness in Honeybee Hives from Audio Signals Using Machine Learning

by Stenford Ruvinga, Gordon Hunter, Olga Duran and Jean-Christophe Nebel

Electronics 2023, 12(7), 1627; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12071627 - 30 Mar 2023

Cited by 3 | Viewed by 1543

Honeybees are vital to both the agricultural industry and the wider ecological system, most importantly for their role as major pollinators of flowering plants, many of which are food crops. Honeybee colonies are dependent on having a healthy queen for their long-term survival [...] Read more.

Honeybees are vital to both the agricultural industry and the wider ecological system, most importantly for their role as major pollinators of flowering plants, many of which are food crops. Honeybee colonies are dependent on having a healthy queen for their long-term survival since the queen bee is the only reproductive female in the colony. Thus, as the death or loss of the queen is of great negative impact for the well-being of a honeybee colony, beekeepers need to be aware if a queen has died in any of their hives so that appropriate remedial action can be taken. In this paper, we describe our approaches to using acoustic signals recorded in beehives and machine learning algorithms to identify whether beehives do or do not contain a healthy queen. Our results are extremely positive and should help beekeepers decide whether intervention is needed to preserve the colony in each of their hives. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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12 pages, 2162 KiB

Open AccessArticle

A Mobile App for Supporting Citrus Fruit Growers in Greece

by Sotirios Karetsos, Constantina Costopoulou, Nefeli Gourdomichali and Maria Ntaliani

Electronics 2022, 11(20), 3342; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11203342 - 17 Oct 2022

Cited by 1 | Viewed by 1385

Abstract

Crop management is a challenging and time-consuming task that involves many different data and farming activities. Data regarding the plots, the crop, and seasonal farming activities, are important for improving crop management for different cultivations. Specifically, citrus fruits are significant cultivations for Greece. [...] Read more.

Crop management is a challenging and time-consuming task that involves many different data and farming activities. Data regarding the plots, the crop, and seasonal farming activities, are important for improving crop management for different cultivations. Specifically, citrus fruits are significant cultivations for Greece. Citrus cultivation is mainly practiced in small-sized, dispersed, family-owned plots and requires cumulative knowledge and experience regarding various activities. Such knowledge has to be easily registered and be made accessible ubiquitously, and should cover various activities for each plot, such as plowing, planting, pruning, spraying, irrigating, harvesting, and the purchase of agricultural supplies. Usually, farmers keep an archive of farming activities on paper, in a non-structured form, from which it is difficult and time consuming to retrieve and use information. The objective of this article is the deployment of a mobile app for citrus management that supports the recording and retrieving of farming data. The app design was based on the requirements of two hundred farmers and its implementation was based on the Android platform. The usability test evaluation shows that the app meets citrus farmers’ requirements. Limitations of this study include the small evaluation sample and the exclusive use of the Greek language. Future work focuses on the development of localized apps for specific crops, combined with other functionalities, such as location-based services, alerts, and cloud data storage, to help farmers with their specific needs in practice. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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11 pages, 3615 KiB

Open AccessArticle

Integration of Low-Cost Digital Tools for Preservation of a Sustainable Agriculture System

by Alejandra Serrano-Trujillo, José Jaime Esqueda-Elizondo and Laura Jiménez-Beristáin

Electronics 2022, 11(6), 964; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11060964 - 21 Mar 2022

Cited by 1 | Viewed by 1966

Abstract

This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the [...] Read more.

This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the health of the growing plants through image processing techniques. This proposal is implemented in a low-cost computer, receiving measurements from five sensors, including a camera, and processing the signals using open-source libraries and software. Periodic measurements of the temperature, water level, light, and pH within the system are collected and shared to a cloud platform that allows their display in a dashboard, accessible through a web page. The health of the vegetables growing in the system is estimated by analyzing visible and infrared spectra, applying feature extraction, and computing vegetation indices. This work provides a low-cost solution for preserving sustainable urban farming systems, suitable for new farming communities. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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13 pages, 735 KiB

Open AccessArticle

Evaporation Forecasting through Interpretable Data Analysis Techniques

by M. Carmen Garrido, José M. Cadenas, Andrés Bueno-Crespo, Raquel Martínez-España, José G. Giménez and José M. Cecilia

Electronics 2022, 11(4), 536; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11040536 - 10 Feb 2022

Cited by 8 | Viewed by 1814

Abstract

Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the [...] Read more.

Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to

0.85 R^{2}

and

0.6 M A E

. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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19 pages, 11981 KiB

Open AccessArticle

Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology

by Ivan Mezei, Milan Lukić, Lazar Berbakov, Bogdan Pavković and Boris Radovanović

Electronics 2022, 11(3), 356; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030356 - 25 Jan 2022

Cited by 7 | Viewed by 3397

Abstract

One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting [...] Read more.

One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos^® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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18 pages, 8762 KiB

Open AccessFeature PaperArticle

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

by Francisco Javier López-Andreu, Juan Antonio López-Morales, Manuel Erena, Antonio F. Skarmeta and Juan A. Martínez

Electronics 2022, 11(3), 325; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030325 - 20 Jan 2022

Cited by 9 | Viewed by 1960

Abstract

The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications [...] Read more.

The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications continuously for an agricultural year. This paper presents a generic methodology developed for implementing monitoring controls. To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest and remote sensing-based biophysical indices. This work focuses on monitoring the helpline associated with rice cultivation, using 13 Sentinel-2 images whose grouping and characteristics change depending on the event or landmark being sought. Moreover, the functionality to check, before harvesting the crop, that the area declared is equal to the area cultivated is added. The 2020 results are around 96% for most of the metrics analysed, demonstrating the potential of Sentinel-2 for controlling subsidies, particularly for rice. After the quality assessment, the hit rate is 98%. The methodology is transformed into a tool for regular use to improve decision making by determining which declarants comply with the crop-specific aid obligations, contributing to optimising the administrations’ resources and a fairer distribution of funds. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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18 pages, 2150 KiB

Open AccessArticle

Dairy Farm Management Information Systems

by Ayalew Kassahun, Robbin Bloo, Cagatay Catal and Alok Mishra

Electronics 2022, 11(2), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11020239 - 12 Jan 2022

Cited by 5 | Viewed by 6680

Abstract

Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the [...] Read more.

Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the Dutch dairy sector and present the state–of–the–art. We performed a multivocal literature review (MLR) to find sources both in scientific and grey literature. A grey literature search was adopted because most of the FMISs were not reported in the scientific literature. To support and improve the effectiveness of the MLR process, an online survey was first sent to Dutch dairy farmers to identify the FMISs that are being used in practice. With the help of the MLR process, we identified 50 FMISs used by Dutch dairy farmers. We identified 33 features of these FMISs and listed the advantages and disadvantages of the FMISs. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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14 pages, 3327 KiB

Open AccessArticle

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

by Wanhyun Cho, Sangkyuoon Kim, Myunghwan Na and Inseop Na

Electronics 2021, 10(13), 1576; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10131576 - 30 Jun 2021

Cited by 11 | Viewed by 2450

Abstract

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on [...] Read more.

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO₂ level. Full article

(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)

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