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Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 36421

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Dr. Paul Kwan

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Guest Editor

School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia
Interests: artificial intelligence; pattern recognition; computer vision; machine learning; computational science; data science; digital agriculture; agroinformatics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Benoit Mercatoris

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TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030 Gembloux, Belgium
Interests: machine vision-based crop phenotyping; sensing technology for precision agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Leifeng Guo

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Guest Editor

Agricultural Information Institute of Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South St., Haidian District, Beijing 100086, China
Interests: sensors; drone; precision livestock management; smart agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors are devices that detect and respond to input from the environment. Any measurable quantity, such as humidity, temperature or pressure, can be used as an input. The output is typically data that have been transformed and processed to aid decision-making. In particularly, sensors that are applied in applications in precision agriculture and smart farming are called agriculture sensors. There has been exponential uptake in Agriculture sensors in various environments in recent times, including weather stations, unmanned aerial vehicles, land robots, soil moisture probes etc which can be controlled by mobile apps through both long or short range wireless internet connectivity. These sensors form the backbone of cloud, edge and fog networks which result in the growth of Industrial Internet of Things (IIoT) technologies.

Agriculture sensors that have been used in precision agriculture and smart farming include location sensors, optical sensors, multispectral sensors, thermal infrared sensors, laser sensors, electro-chemical sensors, mechanical sensors, dielectric soil moisture sensors, air flow sensors, just to name several. Applications of these sensors can be in determining latitude, longitude and altitude of any position in a measured area, taking help from signals from GPS satellites. They can be used to measure properties of crops either through satellites, drones or robots to estimate the growth stages, physical height, yield, stress of the crops. Some of them can gather chemical data of the soils by detecting special ions in the soil thereby providing information on pH, moisture content, organic matter and soil nutrient levels. Other applications can include measuring soil compaction, moisture levels using dielectric constant of the soil, air permeability, etc. There are also widely applications in the livestock monitoring areas based on acceleration sensors and location sensors.

A major goal for deploying agriculture sensors is to satisfy increasing global demand for food by maximizing yields using minimum resources like water, fertiliser, seeds, etc. To be more widely adopted, these sensors should be simple to use and easy to install to reduce the steep learning curve faced by farmers who are often not trained in the latest hardware and software technologies. Although much progress has been made in recent times on agriculture sensors and their peripheral technologies, there are still many unsolved problems that awaits solutions proposed by both academia and industry, and their inter-disciplinary collaborations.

This Special Issue will showcase the latest advancements in agriculture sensor technologies and their utilisation in innovative digital agriculture applications across multiple data sources and resolutions. Contributions are welcomed to address key and emergent R&D issues including, but are not limited to, crop phenotyping, yield estmation, livestock monitoring, remote sensing for soil quality assessment, autonomous robots and UAV vision systems for site-specific farming, multisensor data fusion, sensor networks and connectivity, animal health and welfare, smart greenhouse, climate monitoring, irrigation monitoring, just to name several.

Prof. Dr. Paul Kwan
Prof. Dr. Benoit Mercatoris
Dr. Leifeng Guo
Guest Editors

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Keywords

precision agriculture
smart farming
agriculture sensors
artificial intelligence
internet of things
machine learning
computer vision
field robots
autonomous systems
cloud computing
edge computing
fog computing
remote sensing
cyber-physical systems
multisensor platforms
supply chain
risk management
crop phenotyping
unmmaned aerial vehicles
deep learning
soil health
yield estimation
livestock monitoring
site-specific farming
sensor networks
multispectral
thermal infrared
animal health
animal welfare
smart greenhouse
climate monitoring
irrigation monitoring

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Published Papers (12 papers)

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21 pages, 8877 KiB

Open AccessArticle

Real-Time Detection of Strawberry Ripeness Using Augmented Reality and Deep Learning

by Jackey J. K. Chai, Jun-Li Xu and Carol O’Sullivan

Sensors 2023, 23(17), 7639; https://doi.org/10.3390/s23177639 - 3 Sep 2023

Cited by 4 | Viewed by 3525

Abstract

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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11 pages, 1117 KiB

Open AccessArticle

Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy

by Maylin Acosta, Ana Quiñones, Sandra Munera, José Miguel de Paz and José Blasco

Sensors 2023, 23(14), 6530; https://doi.org/10.3390/s23146530 - 19 Jul 2023

Cited by 5 | Viewed by 2067

Abstract

The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 ‘Clementina de Nules’ citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430–1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430–750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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14 pages, 4861 KiB

Open AccessArticle

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

by Chengxiang Jiang, Jingwei Qi, Tianci Hu, Xin Wang, Tao Bai, Leifeng Guo and Ruirui Yan

Sensors 2023, 23(13), 5831; https://doi.org/10.3390/s23135831 - 22 Jun 2023

Cited by 2 | Viewed by 1475

Abstract

Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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17 pages, 5373 KiB

Open AccessArticle

Grazing Sheep Behaviour Recognition Based on Improved YOLOV5

by Tianci Hu, Ruirui Yan, Chengxiang Jiang, Nividita Varun Chand, Tao Bai, Leifeng Guo and Jingwei Qi

Sensors 2023, 23(10), 4752; https://doi.org/10.3390/s23104752 - 15 May 2023

Cited by 8 | Viewed by 3072

Abstract

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP_@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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16 pages, 5989 KiB

Open AccessArticle

Fuzzy Control Application to an Irrigation System of Hydroponic Crops under Greenhouse: Case Cultivation of Strawberries (Fragaria Vesca)

by Edgar Maya Olalla, Andres Lopez Flores, Marcelo Zambrano, Mauricio Domínguez Limaico, Henry Diaz Iza and Carlos Vasquez Ayala

Sensors 2023, 23(8), 4088; https://doi.org/10.3390/s23084088 - 18 Apr 2023

Cited by 5 | Viewed by 2830

Abstract

Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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12 pages, 1707 KiB

Open AccessArticle

Vertical Farming Monitoring: How Does It Work and How Much Does It Cost?

by Paula Morella, María Pilar Lambán, Jesús Royo and Juan Carlos Sánchez

Sensors 2023, 23(7), 3502; https://doi.org/10.3390/s23073502 - 27 Mar 2023

Cited by 10 | Viewed by 5288

Abstract

Climate change, resource scarcity, and a growing world population are some of the problems facing traditional agriculture. For this reason, new cultivation systems are emerging, such as vertical farming. This is based on indoor cultivation, which is not affected by climatic conditions. However, vertical farming requires higher consumption of water and light, since in traditional agriculture those resources are free. Vertical cultivation requires the use of new technologies and sensors to reduce water and energy consumption and increase its efficiency. The sensorization of these systems makes it possible to monitor and evaluate their performance in real time. In addition, vertical farming faces economic uncertainty since its profitability has not been studied in depth. This article studies the most important variables when monitoring a vertical farming system and proposes the sensors to be used in the data acquisition system. In addition, this study presents a cost model for the installation of this type of system. This cost model is applied to a case study to evaluate the profitability of installing this type of infrastructure. The results obtained suggest that the investment made in VF installations could be profitable in a period of three to five years. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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15 pages, 33254 KiB

Open AccessArticle

Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

by Hui Shao, Fuyu Wang, Wei Li, Peilun Hu, Long Sun, Chong Xu, Changhui Jiang and Yuwei Chen

Sensors 2023, 23(6), 3286; https://doi.org/10.3390/s23063286 - 20 Mar 2023

Cited by 4 | Viewed by 2017

Abstract

Intelligent management of trees is essential for precise production management in orchards. Extracting components’ information from individual fruit trees is critical for analyzing and understanding their general growth. This study proposes a method to classify persimmon tree components based on hyperspectral LiDAR data. We extracted nine spectral feature parameters from the colorful point cloud data and performed preliminary classification using random forest, support vector machine, and backpropagation neural network methods. However, the misclassification of edge points with spectral information reduced the accuracy of the classification. To address this, we introduced a reprogramming strategy by fusing spatial constraints with spectral information, which increased the overall classification accuracy by 6.55%. We completed a 3D reconstruction of classification results in spatial coordinates. The proposed method is sensitive to edge points and shows excellent performance for classifying persimmon tree components. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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21 pages, 6156 KiB

Open AccessArticle

Optimization of the Outlet Shape of an Air Circulation System for Reduction of Indoor Temperature Difference

by Jin-Young Park, Young-Jun Yoo and Young-Choon Kim

Sensors 2023, 23(5), 2570; https://doi.org/10.3390/s23052570 - 25 Feb 2023

Cited by 1 | Viewed by 1577

Abstract

This study proposes an air circulation system that can forcibly circulate the lowest cold air to the top of indoor smart farms, and it has a width, length, and height of 6, 12, and 2.5 m, respectively, to reduce the effect of temperature differences between the upper and lower parts on the growth rate of plants in winter. This study also aimed to reduce the temperature deviation generated between the upper and lower parts of the target indoor space by optimizing the shape of the manufactured outlet of the air circulation system. A table of L9 orthogonal arrays, which is a design of experiment methodology, was used, and it presented three levels of the following design variables: blade angle, blade number, output height, and flow radius. Flow analysis was performed for the experiments on the nine models to minimize the high time and cost requirements. Based on the derived analysis results, an optimized prototype was manufactured by applying the Taguchi method, and experiments were conducted by installing 54 temperature points in an indoor space to identify the temperature difference between the upper and lower parts over time for the performance experiment. Under natural convection, the minimum temperature deviation was 2.2 °C and the temperature difference between the upper and lower parts did not decrease. For a model without an outlet shape, such as a vertical fan, the minimum temperature deviation was 0.8 °C and at least 530 s were required to reach a difference of less than 2 °C. When air was circulated in the air circulation system with the proposed outlet shape, the minimum temperature deviation was 0.6 °C and the time required to reach a difference of less than 2 °C was 440 s. Using the proposed air circulation system, cooling and heating costs are expected to be reduced in summer and winter because the arrival time and temperature difference between the upper and lower parts can be reduced using the outlet shape compared with the case without the outlet shape. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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12 pages, 433 KiB

Open AccessArticle

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

by Xanno Sigalingging, Setya Widyawan Prakosa, Jenq-Shiou Leu, He-Yen Hsieh, Cries Avian and Muhamad Faisal

Sensors 2023, 23(3), 1358; https://doi.org/10.3390/s23031358 - 25 Jan 2023

Cited by 3 | Viewed by 2059

Abstract

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves

88.72 %

accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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20 pages, 6083 KiB

Open AccessArticle

Monitoring of Indoor Farming of Lettuce Leaves for 16 Hours Using Electrical Impedance Spectroscopy (EIS) and Double-Shell Model (DSM)

by Joseph Christian Nouaze, Jae Hyung Kim, Gye Rok Jeon and Jae Ho Kim

Sensors 2022, 22(24), 9671; https://doi.org/10.3390/s22249671 - 10 Dec 2022

Cited by 4 | Viewed by 2943

Abstract

An electrical impedance spectroscopy (EIS) experiment was performed using a double-shell electrical model to investigate the feasibility of detecting physiological changes in lettuce leaves over 16 h. Four lettuce plants were used, and the impedance spectra of the leaves were measured five times per plant every hour at frequencies of 500 Hz and 300 kHz. Estimated R-C parameters were computed, and the results show that the lettuce leaves closely fit the double-shell model (DSM). The average resistance ratios of R₁ = 10.66R₄ and R₁ = 3.34R₂ show high resistance in the extracellular fluid (ECF). A rapid increase in resistance (R₁, R₂, and R₄) and a decrease in capacitance (C₃ and C₅) during water uptake were observed. In contrast, a gradual decrease in resistance and an increase in capacitance were observed while the LED light was on. Comparative studies of leaf physiology and electrical value changes support the idea that EIS is a great technique for the early monitoring of plant growth for crop production. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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13 pages, 2927 KiB

Open AccessArticle

Precision Seeding Compensation and Positioning Based on Multisensors

by Jiaze Sun, Yan Zhang, Yuting Zhang, Peize Li and Guifa Teng

Sensors 2022, 22(19), 7228; https://doi.org/10.3390/s22197228 - 23 Sep 2022

Cited by 4 | Viewed by 1616

Abstract

The current multi-row planter always leads to uneven seeding spacing between rows while seeding in curve paths, which causes uneven growth, a cost increase of production and management, and reduced yield. With the development of smart farming technology, a curve seeding compensation and precise positioning model is proposed in the paper to calculate the real-time speed and position of each seeding unit based on the information from multisensors, such as GNSS and IMU, and to predict the next seeding position to achieve uniform seeding on the curve and improve the unit yield of crops. MATLAB Simulink simulation experiments show that the seeding pass rate of the model is 99.97% when the positioning accuracy is

\pm 0.01 m

and the traction speed is 1 m/s, and the seeding pass rate of the five-row seeder is as high as 99.81% when the traction speed is 3 m/s, which verifies the effectiveness and practicality of the model. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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20 pages, 1581 KiB

Open AccessSystematic Review

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

by Preety Baglat, Ahatsham Hayat, Fábio Mendonça, Ankit Gupta, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias

Sensors 2023, 23(2), 738; https://doi.org/10.3390/s23020738 - 9 Jan 2023

Cited by 12 | Viewed by 4669

Abstract

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued. Full article

(This article belongs to the Special Issue Advances in Agriculture Sensor Technologies and Their Applications in Precision Agriculture and Smart Farming)

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