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Sensors and Data-Driven Precision Agriculture

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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 (20 January 2024) | Viewed by 28869

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Prof. Dr. Takaharu Kameoka

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Specially Appointed Professor, Interdisciplinary Cluster for Cutting Edge Research Research Center for Social Systems, Shinshu University, Matsumoto, Japan
Interests: agricultural; food and bioinformation engineering; optical foodomics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kazuki Kobayashi

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Faculty of Engineering, Shinshu University, 4-17 Wakasato, Nagano 3808553, Japan
Interests: field monitoring and artificial intelligence based phenotyping
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Dr. Wei Guo

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Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan
Interests: agricultural informatics; plant phenomics; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kotaro Takayama

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Graduate School of Engineering, Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan
Interests: plant diagnosis with sensor technology and AI for agricultural production in greenhouse and indoor farming

Special Issue Information

Dear Colleagues,

Now that agricultural machinery has become computerized and data-integrated systems are being realized, the data-driven nature of precision agriculture, which was originally built as part of a mechanized system, has become clear. Measuring crop vigor, which used to be extremely difficult, is gradually becoming possible with the evolution of multiband optical phenotyping, drones, and robots.

In light of the current state of agriculture, this Special Issue attempts to provide a glimpse into the forefront of science-based data-driven agriculture. For example, the development of multimodal sensors for measuring the soil environment, including soil microorganisms, and MEMS multispectroscopic devices for measuring the light environment that contribute to photosynthesis and photomorphogenesis, or scenarios for measuring and controlling both the environment and crops in institutional cultivation, where environmental control is possible. Other open field research topics include measurement of the growing environment, phenotyping and control strategies using 2D/3D image sensing and machine learning; measurement of communication between soil microorganisms and crops using advanced sensors.

Prof. Dr. Takaharu Kameoka
Prof. Dr. Kazuki Kobayashi
Dr. Wei Guo
Prof. Dr. Kotaro Takayama
Guest Editors

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Keywords

multimodal sensors
MEMS multispectroscopic devices
phenotyping
drones
robot
photosynthesis
precision agriculture

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Published Papers (11 papers)

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Research

18 pages, 4359 KiB

Open AccessArticle

Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops

by Beomjin Park, Seunghwan Wi, Hwanjo Chung and Hoonsoo Lee

Sensors 2024, 24(5), 1442; https://doi.org/10.3390/s24051442 - 23 Feb 2024

Cited by 3 | Viewed by 2039

Abstract

The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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30 pages, 2477 KiB

Open AccessArticle

Formal Assessment of Agreement and Similarity between an Open-Source and a Reference Industrial Device with an Application to a Low-Cost pH Logger

by Evmorfia P. Bataka, Persefoni Maletsika and Christos T. Nakas

Sensors 2024, 24(2), 490; https://doi.org/10.3390/s24020490 - 12 Jan 2024

Viewed by 2049

Abstract

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports, among others. In this work, an open-source, portable, low-cost pH logger, appropriate for in situ measurements, was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then, it was tested by manually measuring the pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compensation when measuring the pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graphical assessment tools using mixed-effects models. The advantages and disadvantages of interpreting agreement through the proposed procedure are discussed. In our illustration, the indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices improved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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20 pages, 12731 KiB

Open AccessArticle

An Ultrasonic RF Acquisition System for Plant Stems Based on Labview Double Layer Multiple Triggering

by Xin Huang, Danju Lv, Rui Xi, Mingyuan Gao, Ziqian Wang, Lianglian Gu, Wei Li and Yan Zhang

Sensors 2023, 23(16), 7088; https://doi.org/10.3390/s23167088 - 10 Aug 2023

Viewed by 1492

Abstract

Ultrasound is widely used in medical and engineering inspections due to its non-destructive and easy-to-use characteristics. However, the complex internal structure of plant stems presents challenges for ultrasound testing. The density and thickness differences in various types of stems can cause different attenuation of ultrasonic signal propagation and the formation of different echo locations. To detect structural changes in plant stems, it is crucial to acquire complete ultrasonic echo RF signals. However, there is currently no dedicated ultrasonic RF detection equipment for plant stems, and some ultrasonic acquisition equipment has limited memory capacity that cannot store a complete echo signal. To address this problem, this paper proposes a double-layer multiple-timing trigger method, which can store multiple trigger sampling memories to meet the sampling needs of different plant stems with different ultrasonic echo locations. The method was tested in experiments and found to be effective in acquiring complete ultrasonic RF echo signals for plant stems. This approach has practical significance for the ultrasonic detection of plant stems. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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16 pages, 2668 KiB

Open AccessArticle

Reflectance Measurements from Aerial and Proximal Sensors Provide Similar Precision in Predicting the Rice Yield Response to Mid-Season N Applications

by Telha H. Rehman, Mark E. Lundy, Andre Froes de Borja Reis, Nadeem Akbar and Bruce A. Linquist

Sensors 2023, 23(13), 6218; https://doi.org/10.3390/s23136218 - 7 Jul 2023

Cited by 1 | Viewed by 1342

Abstract

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDRE_UAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVI_GS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDRE_UAS and NDVI_GS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDRE_UAS and NDVI_GS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDRE_UAS and NDVI_GS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha⁻¹, respectively. These results indicate that both aerial NDRE_UAS and proximal NDVI_GS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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12 pages, 923 KiB

Open AccessArticle

Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production

by Chris Davison, Craig Michie, Christos Tachtatzis, Ivan Andonovic, Jenna Bowen and Carol-Anne Duthie

Sensors 2023, 23(10), 4621; https://doi.org/10.3390/s23104621 - 10 May 2023

Cited by 3 | Viewed by 4220

Abstract

This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the ‘time spent eating’ data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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19 pages, 5188 KiB

Open AccessArticle

Automatic Branch–Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds

by Haitao Li, Gengchen Wu, Shutian Tao, Hao Yin, Kaijie Qi, Shaoling Zhang, Wei Guo, Seishi Ninomiya and Yue Mu

Sensors 2023, 23(9), 4572; https://doi.org/10.3390/s23094572 - 8 May 2023

Cited by 5 | Viewed by 3613

Abstract

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precision_sem, mean Recall_sem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precision_ins, Recall_ins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm²), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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15 pages, 6203 KiB

Open AccessArticle

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

by Aimi Aznan, Claudia Gonzalez Viejo, Alexis Pang and Sigfredo Fuentes

Sensors 2022, 22(22), 8655; https://doi.org/10.3390/s22228655 - 9 Nov 2022

Cited by 13 | Viewed by 2771

Abstract

Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice’s weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and non-invasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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16 pages, 2708 KiB

Open AccessArticle

Optimal Water Level Management for Mitigating GHG Emissions through Water-Conserving Irrigation in An Giang Province, Vietnam

by Satoshi Ogawa, Kyosuke Yamamoto, Kenichi Uno, Nguyen Cong Thuan, Takashi Togami and Soji Shindo

Sensors 2022, 22(21), 8418; https://doi.org/10.3390/s22218418 - 2 Nov 2022

Cited by 1 | Viewed by 2097

Abstract

Rational water and fertilizer management approaches and technologies could improve water use efficiency and fertilizer use efficiency in paddy rice cultivation. A promising water-conserving technology for paddy rice farming is the alternate wetting and drying irrigation system, established by the International Rice Research Institute. However, the strategy has still not been widely adopted, because water level measurement is challenging work and sometimes leads to a decrease in the rice yield. For the easy implementation of alternate wetting and drying among farmers, we analyzed a dataset obtained from a farmer’s water management study carried out over a three-year period with three cropping seasons at six locations (n = 82) in An Giang Province, Southern Vietnam. We observed a significant relationship between specific water level management and the rice yield and greenhouse gas emissions during different growth periods. The average water level during the crop period was an important factor in increasing the rice yield and reducing greenhouse gas emissions. The average water level at 2 days after nitrogen fertilization also showed a potential to increase the rice yield. The greenhouse gas emissions were reduced when the number of days of non-flooded soil use was increased by 1 day during the crop period. The results offer insights demonstrating that farmers’ implementation of multiple drainage during whole crop period and nitrogen fertilization period has the potential to contribute to both the rice yield increase and reduction in greenhouse gas emissions from rice cultivation. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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15 pages, 29938 KiB

Open AccessArticle

Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network

by Taewon Moon, Dongpil Kim, Sungmin Kwon, Tae In Ahn and Jung Eek Son

Sensors 2022, 22(20), 7728; https://doi.org/10.3390/s22207728 - 12 Oct 2022

Cited by 8 | Viewed by 2589

Abstract

Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers (Capsicum annuum var. annuum); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R² values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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20 pages, 11236 KiB

Open AccessArticle

Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning

by Takaya Hondo, Kazuki Kobayashi and Yuya Aoyagi

Sensors 2022, 22(17), 6473; https://doi.org/10.3390/s22176473 - 28 Aug 2022

Cited by 4 | Viewed by 2307

Abstract

Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions of fruit. Thus, additional research is required for automatic annotation and tracking fruit changes over time. We propose a system to record the growth characteristics of individual apples in real time using Mask R-CNN. To accurately detect fruit regions hidden behind leaves and other fruits, we developed a region detection model by automatically generating 3000 composite orchard images using cropped images of leaves and fruits. The effectiveness of the proposed method was verified on a total of 1417 orchard images obtained from the monitoring system, tracking the size of fruits in the images. The mean absolute percentage error between the true value manually annotated from the images and detection value provided by the proposed method was less than 0.079, suggesting that the proposed method could extract fruit sizes in real time with high accuracy. Moreover, each prediction could capture a relative growth curve that closely matched the actual curve after approximately 150 elapsed days, even if a target fruit was partially hidden. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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13 pages, 4453 KiB

Open AccessArticle

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

by Yuki Yamagishi, Yoichiro Kato, Seishi Ninomiya and Wei Guo

Sensors 2022, 22(15), 5547; https://doi.org/10.3390/s22155547 - 25 Jul 2022

Cited by 7 | Viewed by 2841

Abstract

The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive stages under flooded conditions. Unlike popular deep-learning-based approaches requiring training data and computational resources, we propose a simple image-processing pipeline following the empirical principles of synchronously emerging leaves and tillers in rice morphogenesis. Field images were taken by an unmanned aerial vehicle at a very low flying height for UAV imaging—1.5 to 3 m above the rice canopy. Subsequently, the proposed image-processing pipeline was used, which includes binarization, skeletonization, and leaf-tip detection, to count the number of long-growing leaves. The tiller number was estimated from the number of long-growing leaves. The estimated tiller number in a 1.1 m × 1.1 m area is significantly correlated with the actual number of tillers, with 60% of hills having an error of less than ±3 tillers. This study demonstrates the potential of the proposed image-sensing-based tiller-counting method to help agronomists with efficient, non-destructive field phenotyping. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)

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