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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (26 August 2023) | Viewed by 24132

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Dr. Simone Priori

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Guest Editor

University of Tuscia, Department of Agriculture and Forest Sciences (DAFNE), Viterbo, Italy
Interests: pedology; digital soil mapping; proximal sensing; soil hydrology
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Dr. Antonello Bonfante

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Guest Editor

Consiglio Nazionale delle Ricerche (CNR), Istituto per i sistemi Agricoli e Forestali del Mediterraneo, Via Patacca, 85 - 80056 Ercolano, Napoli, Italy
Interests: hydropedology; precision agriculture; crop adaptation to climate change
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Dr. Anna Brook

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Guest Editor

Spectroscopy and Remote Sensing Laboratory, Department of Geography and Environmental Studie, Faculty of Social Science, University of Haifa, Haifa 3498838, Israel
Interests: data fusion; image and signal processing; automation target recognition; sub-pixel detection; spectral models across NIR-MIR regions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote and proximal sensing technologies enable the acquisition of diverse spatial data both in agriculture and in forestry, and they represent one of the pillars of the digital agriculture and forestry. New-generation satellites hosting hyperspectral cameras (e.g., Hyperion, EnMap, Shalom, Prisma) along with price decreases in multi- and hyperspectral cameras as well as thermal cameras for airborne and UAV platforms have laid the foundation for important steps ahead in land monitoring. In terms of proximal sensing, innovative platforms ranging from handheld, robotics, and tractor-embedded sensors have been developed in recent years. This Special Issue calls for original and innovative manuscripts related to recent research and activities that demonstrate the proficient use of remote and/or proximal sensing techniques in agriculture and forestry. The topics of the submitted manuscripts include:

The applications of innovative sensors or technologies for soil, crops, and forest monitoring
Uncertainty and accuracy of remote/proximal sensing techniques
Multisource data integration
Predictive models based on remote and/or proximal sensing data
Comparisons of different techniques
Remotely and proximally sensed-assisted agricultural practices
Remote sensing of forest disturbances (wildfire, droughts, biotic stresses, etc.)

This Special Issue welcomes diverse types of articles including original research, reviews, and perspective papers (upon consultation with the Editors).

Sincerely,

Dr. Simone Priori
Dr. Antonello Bonfante
Dr. Anna Brook
Guest Editors

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Keywords

satellite images
hyperspectral
thermal images
drones
digital soil mapping
crop monitoring
precision agriculture
sensors for agriculture
forest monitoring

Published Papers (11 papers)

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Research

15 pages, 5153 KiB

Open AccessArticle

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

by Halmat S. Khalaf, Yaseen T. Mustafa and Mohammed A. Fayyadh

Appl. Sci. 2023, 13(19), 10666; https://doi.org/10.3390/app131910666 - 25 Sep 2023

Cited by 2 | Viewed by 1058

Abstract

Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R²), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R² = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R² = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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22 pages, 5270 KiB

Open AccessArticle

An IoT-Based System for Efficient Detection of Cotton Pest

by Saeed Azfar, Adnan Nadeem, Kamran Ahsan, Amir Mehmood, Muhammad Shoaib Siddiqui, Muhammad Saeed and Mohammad Ashraf

Appl. Sci. 2023, 13(5), 2921; https://doi.org/10.3390/app13052921 - 24 Feb 2023

Cited by 6 | Viewed by 2575

Abstract

Considering the importance of cotton products, timely identification of pests (flying moths—being a significant threat to cotton crops) helps to protect cotton crops and improve their production and quality. This study proposes real-time detection of Cotton Flying Moths (CFMs) with the assistance of an Internet of Things (IoT)-based system in the agricultural field. The proposed prototype contains a group of sharp infrared sensors, a Zigbee-based communication module, an Arduino 2560 Mega board, a lithium polymer battery (to power the mote), a gateway device, and an unmanned aerial vehicle (UAV) to respond as a pesticide-sprayer against the detected pest. The proposed pest detection algorithm detects the flying insects’ presence by monitoring variations in the reflected light. Based on this, it sends a detection alert to the gateway device. The gateway device sends detection coordinates to the drone/UAV to respond by spraying pesticide in the detection region. A real testbed and simulation scenarios were implemented to evaluate the effectiveness of the proposed detection system. The results of the testbed implementation suggest the effectiveness of the sensor design and CFM detection. Initial results from the simulation study indicate the suitability of the proposed prototype deployment in the agricultural field. The proposed prototype would not only help minimize the use of pesticides but also maintain the quality and quantity of cotton products. The originality of this study is the custom-made and cost-effective IoT prototype for CFM detection in the agricultural field. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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18 pages, 7332 KiB

Open AccessArticle

Distribution Quality of Agrochemicals for the Revamping of a Sprayer System Based on Lidar Technology and Grapevine Disease Management

by Alessio Ilari, Simone Piancatelli, Luana Centorame, Marwa Moumni, Gianfranco Romanazzi and Ester Foppa Pedretti

Appl. Sci. 2023, 13(4), 2222; https://doi.org/10.3390/app13042222 - 09 Feb 2023

Cited by 2 | Viewed by 1516

Abstract

Grapevines are one of the most intensely treated crops with a high potential risk to health and biodiversity. Thus, the distribution control of agrochemicals is crucial to obtain a high quality and sustainable product for intensive viticulture. Although the search for systems to reduce the waste of chemical products is consistent in some countries, such as Italy, the machinery used are obsolete. The development of an upgrading system for sprayers can be a good compromise to achieve the pollution reduction without requiring huge investments. Field tests were conducted using a LIDAR-based prototype coupled to a commercial sprayer. This study tested the distribution performance using water-sensitive papers and evaluated the infections of grapevine downy and powdery mildews. The results showed a distribution in the vegetation gaps with a higher frequency in the coverage classes >20% in the standard treatment and 10–15% in the LIDAR treatment. Treatments performed with LiDAR reduced the incidence of downy mildew and severity of powdery mildew. The innovative sprayer reduces the distribution of agrochemicals thanks to the on/off control of the nozzles in the voids of vegetation and, meanwhile, controls vineyard fungal disease, so it can be a good way to meet the sustainability and quality of the production. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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19 pages, 6522 KiB

Open AccessArticle

Tree Species Classification Based on Self-Supervised Learning with Multisource Remote Sensing Images

by Xueliang Wang, Nan Yang, Enjun Liu, Wencheng Gu, Jinglin Zhang, Shuo Zhao, Guijiang Sun and Jian Wang

Appl. Sci. 2023, 13(3), 1928; https://doi.org/10.3390/app13031928 - 02 Feb 2023

Cited by 5 | Viewed by 1376

Abstract

In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the information of plenty multisource remote sensing images and self-supervised learning methods. Based on hyperspectral images (HSI) and multispectral images (MSI), the features were extracted by combining generative learning methods with contrastive learning methods. Two kinds of multisource encoders named MAAE (multisource AAE encoder) and MVAE (multisource VAE encoder) were proposed, respectively, which set up pretext tasks to extract multisource features as data augmentation. Then the features were discriminated by the depth-wise cross attention module (DCAM) to enhance effective ones. At last, joint self-supervised methods output the tress species classification map to find the trade-off between providing negative samples and reducing the amount of computation. The M-SSL model can learn more representative features in downstream tasks. By employing the feature cross-fusion process, the low-dimensional information of the data is simultaneously learned in a unified network. Through the validation of three tree species datasets, the classification accuracy reached 78%. The proposed method can obtain high-quality features and is more suitable for label-less tree species classification. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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21 pages, 8028 KiB

Open AccessArticle

Tunicate Swarm Algorithm with Deep Learning Based Land Use and Cover Change Detection in Nallamalla Forest India

by K. Lavanya, Anand Mahendran, Ramani Selvanambi, Manuel Mazzara and Jude D Hemanth

Appl. Sci. 2023, 13(2), 1173; https://doi.org/10.3390/app13021173 - 15 Jan 2023

Cited by 4 | Viewed by 1724

Abstract

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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35 pages, 8758 KiB

Open AccessArticle

Soil Salinity Prediction and Its Severity Mapping Using a Suitable Interpolation Method on Data Collected by Electromagnetic Induction Method

by Yuratikan Jantaravikorn and Suwit Ongsomwang

Appl. Sci. 2022, 12(20), 10550; https://doi.org/10.3390/app122010550 - 19 Oct 2022

Cited by 2 | Viewed by 1478

Abstract

Salt mining and shrimp farming have been practiced in the Non Thai district and the surrounding areas for more than 30 years, creating saline soil problems. To solve the soil salinity problem, soil salinity prediction and mapping utilizing the electromagnetic induction method (EMI) and spatial interpolation methods were examined in the Non Thai district, Nakhon Ratchasima province, Thailand. The research objectives were (1) to predict soil salinity using spatial interpolation methods and (2) to identify a suitable spatial interpolation method for soil salinity severity mapping. The research methodology consisted of five steps: apparent electrical conductivity (ECa) measurement using an electromagnetic induction (EMI) method; in situ soil sample collection and electrical conductivity of the saturated soil paste extract (ECe) measurement; soil electrical conductivity estimation using linear regression analysis (LRA); soil salinity prediction and accuracy assessment; and soil salinity severity classification and overlay analysis with relevant data. The result of LRA showed a strong positive relationship between ECe and ECa. The correlation coefficient (R) values of a horizontal measuring mode (HH) and a vertical measuring mode (VV) were 0.873 to 0.861, respectively. Four selected interpolation methods—Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Ordinary CoKriging (OCK) with soil moisture content, and Regression Kriging (RK) without covariable factor—provided slightly different patterns of soil salinity prediction with HH and VV modes. The mean values of the ECe prediction from the four methods at the district level varied from 2156.02 to 2293.25 mS/m for HH mode and from 2377.38 to 2401.41 mS/m for VV mode. Based on the accuracy assessment with the rank-sum technique, the OCK is a suitable interpolation method for soil salinity prediction for HH mode. At the same time, the IDW is suitable for soil salinity prediction for the VV mode. The dominant soil salinity severity classes of the two measuring modes using suitable spatial interpolation methods were strongly and very strongly saline. Consequently, the developed research methodology can be applied to conduct soil salinity surveys to reduce costs and save time in other areas by government agencies in Thailand. Nevertheless, to apply the EMI method for soil salinity survey, the users should understand the principle of EMI and how to calibrate and operate the EM device properly for accurate ECa measurement. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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16 pages, 18975 KiB

Open AccessArticle

Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard

by Giovanni Caruso, Giacomo Palai, Riccardo Gucci and Simone Priori

Appl. Sci. 2022, 12(3), 1309; https://doi.org/10.3390/app12031309 - 26 Jan 2022

Cited by 13 | Viewed by 1876

Abstract

The aim of this study was to evaluate the potential use of remote and proximal sensing techniques to identify homogeneous zones in a high density irrigated olive (Olea europaea L.) orchard subjected to three irrigation regimes (full irrigation, deficit irrigation and rainfed conditions). An unmanned aerial vehicle equipped with a multispectral camera was used to measure the canopy NDVI and two different proximal soil sensors to map soil spatial variability at high resolution. We identified two clusters of trees showing differences in fruit yield (17.259 and 14.003 kg per tree in Cluster 1 and 2, respectively) and annual TCSA increment (0.26 and 0.24 dm², respectively). The higher tree productivity measured in Cluster 1 also resulted in a higher water use efficiency for fruit (WUE_f of 0.90 g dry weight L⁻¹ H₂O) and oil (WUE_o of 0.32 g oil L⁻¹ H₂O) compared to Cluster 2 (0.67 and 0.27 for WUE_f and WUE_o, respectively). Remote and proximal sensing technologies allowed to determine that: (i) the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard; (ii) tree vigour played a major role in determining the final fruit yield under optimal soil water availability, whereas soil features prevailed under rainfed conditions. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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24 pages, 5241 KiB

Open AccessArticle

Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

by Changchun Li, Yilin Wang, Chunyan Ma, Weinan Chen, Yacong Li, Jingbo Li, Fan Ding and Zhen Xiao

Appl. Sci. 2021, 11(24), 12164; https://doi.org/10.3390/app112412164 - 20 Dec 2021

Cited by 4 | Viewed by 2702

Abstract

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R² of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R² = 0.74), and the R² of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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13 pages, 6064 KiB

Open AccessArticle

Real-Time Remote Sensing of the Lobesia botrana Moth Using a Wireless Acoustic Detection Sensor

by Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth, Francisco Lazcano, Francisco Santibanez, Nelson Baker, David Castro and Carolina Yáñez

Appl. Sci. 2021, 11(24), 11889; https://doi.org/10.3390/app112411889 - 14 Dec 2021

Cited by 4 | Viewed by 2078

Abstract

This article presents a wireless sensor for pest detection, specifically the Lobesia botrana moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying Lobesia botrana moth. Once a Lobesia botrana moth is detected, the information about the time, geographical location of the sensor and the number of detection events is sent to a server that gathers the detection statistics in real-time. To detect the Lobesia botrana, its acoustic signal was previously characterized in a controlled environment, obtaining its power spectral density for the acoustic filter design. The sensor is tested in a controlled laboratory environment where the detection of the flying moths is successfully achieved in the presence of all types of environmental noises. Finally, the sensor is installed on a vineyard in a region where the moth has already been detected. The device is able to detect flying Lobesia botrana moths during its flying period, giving results that agree with traditional field traps. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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19 pages, 7760 KiB

Open AccessArticle

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

by Huaqiao Xing, Jingge Niu, Chang Liu, Bingyao Chen, Shiyong Yang, Dongyang Hou, Linye Zhu, Wenjun Hao and Cansong Li

Appl. Sci. 2021, 11(23), 11348; https://doi.org/10.3390/app112311348 - 30 Nov 2021

Cited by 7 | Viewed by 1624

Abstract

Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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11 pages, 5795 KiB

Open AccessArticle

Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3

by Mohamed Lamine Mekhalfi, Carlo Nicolò, Yakoub Bazi, Mohamad Mahmoud Al Rahhal and Eslam Al Maghayreh

Appl. Sci. 2021, 11(5), 2238; https://doi.org/10.3390/app11052238 - 03 Mar 2021

Cited by 7 | Viewed by 4241

Abstract

Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. The dataset totals 2511 crop circle samples. With a small training set and a relatively large test set, plausible detection rates were obtained, scoring a precision of 1 and a recall of about 0.82 for Mask R-CNN and a precision of 0.88 and a recall of 0.94 regarding YOLOv3. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing Applied to Agriculture and Forest Sciences)

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