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Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture
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Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (13 September 2023) | Viewed by 62149

Special Issue Editors

Prof. Dr. Daniele Sarri

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura, 13-50145 Florence, Italy
Interests: precision and digital agriculture; precision crop protection; mechanization; agricultural machinery; social innovation; technological transfer; safety; precision technology for specialty crops
Prof. Dr. Marco Vieri

E-Mail Website
Guest Editor
Department of Agricultural, Alimentary, Environmental and Forestry Sciences—Division Biosystem Engineering, University of Florence, 50144 Florence, Italy
Interests: sustainable agricultural food production engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gabriel Araújo e Silva Ferraz

E-Mail Website
Guest Editor
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras—UFLA, P.O. Box 3037, Lavras 37200-900, Brazil
Interests: remote sensing; UAV in agriculture and livestock; digital and precision farming and livestock
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Sozzi

E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, Italy
Interests: viticulture; precision and digital agriculture; remote sensing; satellite; gis; object detection; image analysis; viticulture mechanization; agricultural robotics; site-specific management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural sector must urgently respond to a change aimed at optimizing the use of resources, maximizing yields to meet the growing demand for food and raising the quality of some high-value products. These strategies are pursued by the United Nations through Sustainable Development Goals, the European Green Deal and Farm to Fork Strategy, the paradigm of the circular economy, and digitization in agriculture. These objectives must at the same time necessarily face a further challenge, namely that of progressive and in some cases very quick climate changes and the protection of soil, water, and environmental resources that require new ways of managing agricultural production practices. Sustainable Precision Agriculture (SPA) is a strategy that can, if properly implemented, have environmental, social, and economic benefits. However, in many countries, there is still a scarce diffusion and acceptance of the SPA for reasons attributable to the small size of the farm surfaces, the long turnover, the excessive management complexity from data mining to the on-field application, and in some cases the unsuitability of technical solutions.

To overcome these gaps, this Special Issue aims to illustrate the latest engineering applications, technical and operational process innovations that make it possible to reduce input waste (pesticide, nutrients, water, fossil energy), monitor the evolution of operative performance over time with the consequent adjustment based on the data collected, and offer new opportunities for qualitative enhancement of productions. Contributions describing the most recent solutions for agricultural sensing and machinery and implementing monitoring and variable rate management aimed at solving farmers’ problems are invited.

The objective of this Special Issue is to collect contributions of engineering applications that promote sustainable precision agriculture also through case studies describing tools that digitally collect, store, analyze, and share digital data which support the farmer in the decision and operative scenario.

The collected contributions will bring new knowledge and innovations that can be transferred to the producers and will also be of interest to consumers, policymakers, service providers, and researchers.

This Special Issue focuses but is not limited to sustainable precision agriculture in the following main areas:

  • Mechanization and mechatronics;
  • Agricultural machinery;
  • Variable rate equipment;
  • Big data and data analysis applied to agriculture practices;
  • Connectivity; telecommunication in agriculture and telemetry in agriculture machinery;
  • Geotechnologies (GIS, RS, GNSS, photogrammetry, etc.) applied to agriculture;
  • Artificial Intelligence (machine learning and deep learning);
  • Internet of Things (IoT) and traceability;
  • Robotization and automation in agriculture;
  • Sensors and biosensors: development and application in agriculture;
  • Unmanned aerial vehicles;
  • Computer vision and image analysis;
  • Sustainability aspects (environmental–social–economic) of new technologies in agriculture.

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

Prof. Dr. Daniele Sarri
Prof. Dr. Marco Vieri
Dr. Gabriel Araújo e Silva Ferraz
Dr. Marco Sozzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • variable rate
  • automation
  • mechatronics
  • big data
  • telemetry
  • Internet of Things
  • robotization
  • artificial intelligence
  • data management

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Published Papers (17 papers)

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13 pages, 1957 KiB  
Article
Life Cycle Assessment of Winter Wheat Production Using Precision and Conventional Seeding Technologies
by Marius Kazlauskas, Indrė Bručienė, Dainius Savickas, Vilma Naujokienė, Sidona Buragienė, Dainius Steponavičius, Kęstutis Romaneckas and Egidijus Šarauskis
Sustainability 2023, 15(19), 14376; https://doi.org/10.3390/su151914376 - 29 Sep 2023
Cited by 3 | Viewed by 1691
Abstract
Sustainable and responsible agricultural production is one of the keys to keeping people, animals, soil, and the environment healthy. Precision seeding technologies for winter wheat, exploiting the variability of soil properties and adapting the technological processes of variable rate seeding and variable seeding [...] Read more.
Sustainable and responsible agricultural production is one of the keys to keeping people, animals, soil, and the environment healthy. Precision seeding technologies for winter wheat, exploiting the variability of soil properties and adapting the technological processes of variable rate seeding and variable seeding depths, are essential not only to improving plant productivity and economic benefits but also to cleaner agricultural production. This work aimed to carry out a life cycle assessment (LCA) of winter wheat production and determine the environmental impact of different precision seeding technologies in terms of individual impact categories compared to conventional seeding technology. Experimental studies were carried out between 2020 and 2022 using conventional uniform seeding rate (URS) and several precision seeding technologies: in the first year—VRS for variable seeding rate and VRS + VRF for variable seeding rate and fertilizer rate, and in the second year—VRS and VRSD for variable seeding rate and variable depth, and VRSD + VRF for variable seeding rate, variable depth, and variable fertilizer rate. The results obtained for winter wheat grain yield showed that the effect of precision seeding technology on the increase of grain yield was not significant compared to the URS. A greater influence on grain yield was found in individual soil management zones, especially in the zone with the worst soil fertility. The LCA did not show any significant differences between precision seeding technology and conventional technology in any of the environmental impact categories. The GWP values (0.200–0.236 kg CO2eq kg−1) were most dependent on grain yield, as precision seeding technology had small changes in the amount of inputs (seeds and fertilizers), while all other technological operations were the same as under the URS technology. The amounts of phosphorus and potassium fertilizers decreased by 1.4 and 7.9%, respectively, and the amounts of winter wheat seeds and nitrogen fertilizers increased by 4.1 and 5.4%, respectively, compared to the URS. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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15 pages, 15145 KiB  
Article
Estimation of Rubber Yield Using Sentinel-2 Satellite Data
by Niwat Bhumiphan, Jurawan Nontapon, Siwa Kaewplang, Neti Srihanu, Werapong Koedsin and Alfredo Huete
Sustainability 2023, 15(9), 7223; https://doi.org/10.3390/su15097223 - 26 Apr 2023
Cited by 4 | Viewed by 3288
Abstract
Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price [...] Read more.
Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price stability, it is important to estimate the yields in advance. Remote sensing technology can effectively provide large-scale spatial data; however, productivity estimates need to be processed from high spatial resolution data generated from satellites with high accuracy and reliability, especially for smallholder livelihood areas where smaller plots contrast with large farms. This study used reflectance data from Sentinel-2 satellite imagery acquired for the 12 months between December 2020 and November 2021. The imagery included 213 plots where data on rubber production in smallholder agriculture were collected. Six vegetation indices (Vis), namely Green Soil Adjusted Vegetation Index (GSAVI), Modified Simple Ratio (MSR), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Green (NR), and Ratio Vegetation Index (RVI) were used to estimate the rubber yield. The study found that the red edge spectral band (band 5) provided the best prediction with R2 = 0.79 and RMSE = 29.63 kg/ha, outperforming all other spectral bands and VIs. The MSR index provided the highest coefficient of determination, with R2 = 0.62 and RMSE = 39.25 kg/ha. When the red edge reflectance was combined with the best VI, MSR, the prediction model only slightly improved, with a coefficient determination of (R2) of 0.80 and an RMSE of 29.42 kg/ha. The results demonstrated that the Sentinel-2 data are suitable for rubber yield prediction for smallholder farmers. The findings of this study can be used as a guideline to apply in other countries or areas. Future studies will require the use of reflectance and vegetation indices derived from satellite data in combination with meteorological data, as well as the application of complex models, such as machine learning and deep learning. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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17 pages, 7295 KiB  
Article
Headland and Field Edge Performance Assessment Using Yield Maps and Sentinel-2 Images
by Kaihua Liu, Ahmed Kayad, Marco Sozzi, Luigi Sartori and Francesco Marinello
Sustainability 2023, 15(5), 4516; https://doi.org/10.3390/su15054516 - 2 Mar 2023
Cited by 2 | Viewed by 2176
Abstract
Headland and field edges have a higher traffic frequency compared to the field centre, which causes more compaction. Most repeated compaction is located at the field entrance area and headland during machinery turning and material transporting that takes place during the fertilisation, herbicide [...] Read more.
Headland and field edges have a higher traffic frequency compared to the field centre, which causes more compaction. Most repeated compaction is located at the field entrance area and headland during machinery turning and material transporting that takes place during the fertilisation, herbicide laying, and harvesting of fields, which could cause soil structure destruction and yield reduction. In this study, the differences between headland, field edges, and field centre were studied using yield maps and the vegetation indices (VIs) calculated by the Google Earth Engine (GEE). First, thirteen yield maps from 2019 to 2022 were used to measure the yield difference between headland, field edges, and field centre. Then, one hundred and eleven fields from northern Italy were used to compare the vegetation indices (VIs) differences between headland, field edges, and field centre area. Then, field size, sand, and clay content were calculated and estimated from GEE. The yield map showed that headland and field edges were 12.20% and 2.49% lower than the field centre. The results of the comparison of the VIs showed that headlands and field edges had lower values compared to the field centre, with reductions of 4.27% and 2.70% in the normalised difference vegetation index (NDVI), 4.17% and 2.67% in the green normalized difference vegetation index (GNDVI), and 5.87% and 3.59% in the normalised difference red edge (NDRE). Additionally, the results indicated that the yield losses in the headland and field edges increased as the clay content increased and sand content decreased. These findings suggest that soil compaction and structural damage caused by the higher traffic frequency in the headland and field edges negatively affect crop yield. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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27 pages, 4014 KiB  
Article
Impact of Water Meadow Restoration on Forage Hay Production in Different Hydro-Meteorological Conditions: A Case Study of Racot, Central Poland
by Michał Napierała, Mariusz Sojka and Joanna Jaskuła
Sustainability 2023, 15(4), 2959; https://doi.org/10.3390/su15042959 - 6 Feb 2023
Cited by 4 | Viewed by 1995
Abstract
Water meadows in river valleys are a source of very valuable forage. Due to their specificity, an appropriate approach to water management is required. This study assessed the impact of the reclamation of a traditional gravity irrigation system, aimed at saving and reducing [...] Read more.
Water meadows in river valleys are a source of very valuable forage. Due to their specificity, an appropriate approach to water management is required. This study assessed the impact of the reclamation of a traditional gravity irrigation system, aimed at saving and reducing water loss from meadows through controlled drainage. The main purpose of this study was to evaluate the investment in drainage system restoration in the context of improving the yield of fodder hay in water meadows under changing hydrometeorological conditions. The analysis was performed on the basis of meteorological and hydrological data from 30 years in the period 1989–2018. The research was conducted on the basis of two assumptions. The first concerned management of meadows without the use of subsoil irrigation based only on the amount of water supplied from rainfall. The second variant assumed deficit irrigation based on periodic water meadows with systems of ditches and drainage channels that supplied water depending on the currently available amount of water in a nearby river. The field research was performed during the crop season of 2019 and 2020. Drainage restoration investment allowed the amount of water supplied to the meadows to be increased. In the analysed period, on average, almost 30 mm of water was delivered through the ditch system. There was also an increase in hay yields of 32%. However, the investment costs, which amounted to EUR 23,382.48, were too high for this type of farm production. A positive net present value (NPV) was obtained only for 25% of cases of hydrometeorological conditions (first quartile). For the other years, the investment was not profitable. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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13 pages, 5667 KiB  
Article
Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network
by Mengqiang Zhang, Yurong Tang, Hong Zhang, Haipeng Lan and Hao Niu
Sustainability 2023, 15(3), 1744; https://doi.org/10.3390/su15031744 - 17 Jan 2023
Cited by 11 | Viewed by 2065
Abstract
To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity [...] Read more.
To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity was predicted using a neural-network-based model by using the Box–Behnken design (BBD) test. According to the extracted results, the neural network model has a good prediction ability, with the determination coefficient of the model and the mean relative error reaching 0.99 and 2.29%, respectively. The impact of the fertilizer discharge parameter combinations on the discharging performances was examined from both macroscopic and microscopic perspectives. During the fertilizer discharge process, the openness formed between the spiral blades and fertilizer outlet presented periodic changes with the continuous rotation of the spiral blade, thus resulting in the uneven discharge of the fertilizer particles. In addition, there are interacting force chains among fertilizer particles, which are not broken in time during the fertilizer discharge procedure, thus resulting in uneven fertilizer discharge. With comprehensive consideration of the fertilizer discharge efficiency, the fertilizer discharge effect, and CV of the fertilizer discharge uniformity, the spiral parameter combination of the fertilizer discharge after neural network optimization are as follows: rotating speed of 47.6 rpm, blade diameter of 90 mm, pitch of 60 mm, and CV of fertilizer discharge uniformity of 19.05%. Under this optimal spiral parameter combination, the fertilizer discharge effect and discharge efficiency were considered to be relatively good. Our work provides references for the design optimization of the spiral fertilizer applicator and fertilizer discharge parameter combination. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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25 pages, 15014 KiB  
Article
A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture
by Cristiano Fragassa, Giuliano Vitali, Luis Emmi and Marco Arru
Sustainability 2023, 15(2), 998; https://doi.org/10.3390/su15020998 - 5 Jan 2023
Cited by 8 | Viewed by 3257
Abstract
Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing [...] Read more.
Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Specifically, six of the most common machine learning algorithms were applied (i.e., logistic regression, k-nearest neighbors, decision tree, random forest, neural network, and support-vector machine). The proposed method was able to precisely recognize crops and weeds throughout a wide cultivation field, training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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17 pages, 7554 KiB  
Article
Identification and Counting of Coffee Trees Based on Convolutional Neural Network Applied to RGB Images Obtained by RPA
by Lucas Santos Santana, Gabriel Araújo e Silva Ferraz, Gabriel Henrique Ribeiro dos Santos, Nicole Lopes Bento and Rafael de Oliveira Faria
Sustainability 2023, 15(1), 820; https://doi.org/10.3390/su15010820 - 2 Jan 2023
Cited by 3 | Viewed by 3852
Abstract
Computer vision algorithms for counting plants are an indispensable alternative in managing coffee growing. This research aimed to develop an algorithm for automatic counting of coffee plants and to determine the best age to carry out monitoring of plants using remotely piloted aircraft [...] Read more.
Computer vision algorithms for counting plants are an indispensable alternative in managing coffee growing. This research aimed to develop an algorithm for automatic counting of coffee plants and to determine the best age to carry out monitoring of plants using remotely piloted aircraft (RPA) images. This algorithm was based on a convolutional neural network (CNN) system and Open Source Computer Vision Library (OpenCV). The analyses were carried out in coffee-growing areas at the development stages three, six, and twelve months after planting. After obtaining images, the dataset was organized and inserted into a You Only Look Once (YOLOv3) neural network. The training stage was undertaken using 7458 plants aged three, six, and twelve months, reaching stability in the iterations between 3000 and 4000 it. Plant detection within twelve months was not possible due to crown unification. A counting accuracy of 86.5% was achieved with plants at three months of development. The plants’ characteristics at this age may have influenced the reduction in accuracy, and the low uniformity of the canopy may have made it challenging for the neural network to define a pattern. In plantations with six months of development, 96.8% accuracy was obtained for counting plants automatically. This analysis enables the development of an algorithm for automated counting of coffee plants using RGB images obtained by remotely piloted aircraft and machine learning applications. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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16 pages, 2287 KiB  
Article
Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants
by Luana Mendes dos Santos, Gabriel Araújo e Silva Ferraz, Milene Alves de Figueiredo Carvalho, Sabrina Aparecida Teodoro, Alisson André Vicente Campos and Pedro Menicucci Neto
Sustainability 2022, 14(20), 13118; https://doi.org/10.3390/su142013118 - 13 Oct 2022
Cited by 4 | Viewed by 1603
Abstract
Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the [...] Read more.
Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the coffee tree. The inversion method can be used for estimating the canopy chlorophyll content (Chlcanopy) using the leaf chlorophyll content (Chlleaf) and the leaf area index (LAI). The application of vegetation indices (VIs) in high spatial resolution images obtained from remotely piloted aircraft (RPA) can assist in the characterization of Chlcanopy in addition to providing vital and fast information for monitoring crops and aiding decision-making. This study aimed to identify which VIs adequately explain the Chl and evaluate the relationships between the VIs obtained from remotely piloted aircraft (RPA) images and the Chlleaf and Chlcanopy in coffee plants during the wet and dry seasons. The experiment was conducted on a Coffea arabica L. plantation in Lavras, Minas Gerais, Brazil. Images were collected on 26 November 2019 (wet), 11 August 2020 (dry), and 26 August 2021 (dry) by a multispectral camera embedded in a quadcopter. Plant height (H), crow diameter (D), and Chlleaf (a, b and total) data were collected in the field by a metre ruler (H and D) and sensor (Chlleaf). The LAI was calculated based on H and D. The Chlcanopy (a, b, and total) was calculated based on Chlleaf and LAI. The image processing was performed in Pix4D software, and postprocessing and calculation of the 21 VIs were performed in QGIS. Statistical analyses (descriptive, statistical tests, Pearson correlation, residuals calculation, and linear regression) were performed using the software R. The VIs from the RPA that best correlates to Chlcanopy in the wet season were the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2RPA), Modified Simple Ratio (MSRRPA) and Simple Ratio (SRRPA). These VIs had high sensitivity and, therefore, were more affected by chlorophyll variability. For the two dry season studied days, there were no patterns in the relationships between Chlleaf, Chlcanopy, and the VIs. It was possible to use the Chl inversion method for the coffee during the wet season. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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20 pages, 6391 KiB  
Article
Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data
by Soo-Jin Kim, Seung-Jong Bae and Min-Won Jang
Sustainability 2022, 14(18), 11674; https://doi.org/10.3390/su141811674 - 16 Sep 2022
Cited by 33 | Viewed by 4533
Abstract
A linear regression machine learning model to estimate the reference evapotranspiration based on temperature data for South Korea is developed in this study. FAO56 Penman–Monteith (FAO56 P–M) reference evapotranspiration calculated with meteorological data (1981–2021) obtained from sixty-two meteorological stations nationwide is used as [...] Read more.
A linear regression machine learning model to estimate the reference evapotranspiration based on temperature data for South Korea is developed in this study. FAO56 Penman–Monteith (FAO56 P–M) reference evapotranspiration calculated with meteorological data (1981–2021) obtained from sixty-two meteorological stations nationwide is used as the label. All study datasets provide daily, monthly, or annual values based on the average temperature, daily temperature difference, and extraterrestrial radiation. Multiple linear regression (MLR) and polynomial regression (PR) are applied as machine learning algorithms, and twelve models are tested using the training data. The results of the performance evaluation of the period from 2017 to 2021 show that the polynomial regression algorithm that learns the amount of extraterrestrial radiation achieves the best performance (the minimum root-mean-square errors of 0.72 mm/day, 11.3 mm/month, and 40.5 mm/year for daily, monthly, and annual scale, respectively). Compared to temperature-based empirical equations, such as Hargreaves, Blaney–Criddle, and Thornthwaite, the model trained using the polynomial regression algorithm achieves the highest coefficient of determination and lowest error with the reference evapotranspiration of the FAO56 Penman–Monteith equation when using all meteorological data. Thus, the proposed method is more effective than the empirical equations under the condition of insufficient meteorological data when estimating reference evapotranspiration. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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16 pages, 6055 KiB  
Article
Automatic Segmentation and Classification System for Foliar Diseases in Sunflower
by Rodica Gabriela Dawod and Ciprian Dobre
Sustainability 2022, 14(18), 11312; https://doi.org/10.3390/su141811312 - 9 Sep 2022
Cited by 9 | Viewed by 4096
Abstract
Obtaining a high accuracy in the classification of plant diseases using digital methods is limited by the diversity of conditions in nature. Previous studies have shown that classification of diseases made with images of lesions caused by diseases is more accurate than a [...] Read more.
Obtaining a high accuracy in the classification of plant diseases using digital methods is limited by the diversity of conditions in nature. Previous studies have shown that classification of diseases made with images of lesions caused by diseases is more accurate than a classification made with unprocessed images. This article presents the results obtained when classifying foliar diseases in sunflower using a system composed of a model that automatically segments the leaf lesions, followed by a classification system. The segmentation of the lesions was performed using both Faster R-CNN and Mask R-CNN. For the classification of diseases based on lesions, the residual neural networks ResNet50 and ResNet152 were used. The results show that automatic segmentation of the lesions can be successfully achieved in the case of diseases such as Alternaria and rust, in which the lesions are well-outlined. In more than 90% of the images, at least one affected area has been segmented. Segmentation is more difficult to achieve in the cases of diseases such as powdery mildew, in which the entire leaf acquires a whitish color. Diseased areas could not be segmented in 30% of the images. This study concludes that the use of a system composed of a network that segments lesions, followed by a network that classifies diseases, allows us to both more accurately classify diseases and identify those images for which a precise classification cannot be made. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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18 pages, 19078 KiB  
Article
EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture
by Luís Rosado, Pedro Faria, João Gonçalves, Eduardo Silva, Ana Vasconcelos, Cristiana Braga, João Oliveira, Rafael Gomes, Telmo Barbosa, David Ribeiro, Telmo Nogueira, Ana Ferreira and Cristina Carlos
Sustainability 2022, 14(15), 9729; https://doi.org/10.3390/su14159729 - 8 Aug 2022
Cited by 4 | Viewed by 3114
Abstract
Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and [...] Read more.
Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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15 pages, 12509 KiB  
Article
Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture
by Nebojša Nikolić, Pietro Mattivi, Salvatore Eugenio Pappalardo, Cristiano Miele, Massimo De Marchi and Roberta Masin
Sustainability 2022, 14(10), 6324; https://doi.org/10.3390/su14106324 - 22 May 2022
Cited by 2 | Viewed by 2335
Abstract
Weeds are one of the major issues in agricultural production and they are present in most agricultural systems. Due to the heterogeneity of weed distribution, understanding spatial patterns is paramount for precision farming and improving sustainability in crop management. Nevertheless, limited information is [...] Read more.
Weeds are one of the major issues in agricultural production and they are present in most agricultural systems. Due to the heterogeneity of weed distribution, understanding spatial patterns is paramount for precision farming and improving sustainability in crop management. Nevertheless, limited information is currently available about the differences between conventional agricultural (CV) weed spatial patterns and weed spatial patterns in conservation agricultural systems (CA); moreover, opportunities to use unmanned aerial vehicles (UAV) and recognition algorithms to monitor these differences are still being explored and tested. In this work, the opportunity to use UAVs to detect changes in spatial distribution over time between CA and CV fields was assessed for data acquisition. Acquired data were processed using maximum likelihood classification to discriminate between weeds and surrounding elements; then, a similarity assessment was performed using the ‘equal to’ function of the raster calculator. The results show important differences in spatial distribution over time between CA and CV fields. In the CA field 56.18% of the area was infested in both years when the field margin effect was included, and 22.53% when this effect was excluded; on the other hand, in the CV field only 11.50% of the area was infested in both years. The results illustrate that there are important differences in the spatial distribution of weeds between CA and CV fields; such differences can be easily detected using UAVs and identification algorithms combined. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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20 pages, 6334 KiB  
Article
Characterization of Recently Planted Coffee Cultivars from Vegetation Indices Obtained by a Remotely Piloted Aircraft System
by Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Rafael Alexandre Pena Barata, Daniel Veiga Soares, Luana Mendes dos Santos, Lucas Santos Santana, Patrícia Ferreira Ponciano Ferraz, Leonardo Conti and Enrico Palchetti
Sustainability 2022, 14(3), 1446; https://doi.org/10.3390/su14031446 - 27 Jan 2022
Cited by 14 | Viewed by 2466
Abstract
Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was [...] Read more.
Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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Review

Jump to: Research

32 pages, 4269 KiB  
Review
Desalination of Saline Irrigation Water Using Hydrophobic, Metal–Polymer Hydrogels
by David D. J. Antia
Sustainability 2023, 15(9), 7063; https://doi.org/10.3390/su15097063 - 23 Apr 2023
Cited by 1 | Viewed by 1820
Abstract
Saline irrigation water accounts for 15% to 30% of global, anthropogenic, water usage, and around 10% to 15% of global arable food production. Decreasing the salinity of this irrigation water has the potential to substantially increase the yields associated with these crops. In [...] Read more.
Saline irrigation water accounts for 15% to 30% of global, anthropogenic, water usage, and around 10% to 15% of global arable food production. Decreasing the salinity of this irrigation water has the potential to substantially increase the yields associated with these crops. In this paper, 87 sol–gel hydrophobic and supra-hydrophobic, hollow, metal, hydroxyoxide and polymer formulations (constructed using inexpensive, agricultural chemicals) were demonstrated to remove Na+ ions and Cl− ions from saline water. The process operates without producing a waste brine or requiring an external energy source and is designed to desalinate water within existing tanks and impoundments. The desalination results of the polymer were combined with the salinity reduction profiles of 70 crops suitable for cultivation, including arable, orchard, horticultural, and livestock forage crops. The analysis established that use of the desalinated water may result in both substantial increases in crop yield, and an increase in the variety of crops that can be grown. Analysis of the ion removal process established a novel methodology for assessing the salinity of the product water. This methodology allows the salinity of the product water to be determined from a combination of EC (electrical conductivity) and pH measurements. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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18 pages, 2002 KiB  
Review
Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis
by Shaeden Gokool, Maqsooda Mahomed, Richard Kunz, Alistair Clulow, Mbulisi Sibanda, Vivek Naiken, Kershani Chetty and Tafadzwanashe Mabhaudhi
Sustainability 2023, 15(4), 3557; https://doi.org/10.3390/su15043557 - 15 Feb 2023
Cited by 29 | Viewed by 5018
Abstract
In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools [...] Read more.
In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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15 pages, 847 KiB  
Review
Application of GIS in Agriculture in Promoting Evidence-Informed Decision Making for Improving Agriculture Sustainability: A Systematic Review
by Mwehe Mathenge, Ben G. J. S. Sonneveld and Jacqueline E. W. Broerse
Sustainability 2022, 14(16), 9974; https://doi.org/10.3390/su14169974 - 12 Aug 2022
Cited by 16 | Viewed by 14090
Abstract
The objective of this review was to synthesize existing evidence on GIS and RS application in agriculture in enhancing evidence-informed policy and practice for improving agriculture sustainability and identifying obstacles to their application, particularly in low- and middle-income countries. Systematic searches were conducted [...] Read more.
The objective of this review was to synthesize existing evidence on GIS and RS application in agriculture in enhancing evidence-informed policy and practice for improving agriculture sustainability and identifying obstacles to their application, particularly in low- and middle-income countries. Systematic searches were conducted in the databases SCOPUS, Web of Science, Bielefeld Academic Search Engine, COnnecting REpositories (CORE), and Google Scholar. We identified 2113 articles published between 2010–2021, out of which 40 articles met the inclusion criteria. The results show that GIS technology application in agriculture has gained prominence in the last decade, with 66% of selected papers being published in the last six years. The main GIS application areas identified included: crop yield estimation, soil fertility assessment, cropping patterns monitoring, drought assessment, pest and crop disease detection and management, precision agriculture, and fertilizer and weed management. GIS technology has the potential to enhance agriculture sustainability through integrating the spatial dimension of agriculture into agriculture policies. In addition, GIS potential in promoting evidenced informed decision making is growing. There is, however, a big gap in GIS application in sub-Saharan Africa, with only one paper originating from this region. With the growing threat of climate change to agriculture and food security, there is an increased need for the integration of GIS in policy and decision making in improving agriculture sustainability. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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29 pages, 7190 KiB  
Review
A Bibliometric Analysis of the Application of Remote Sensing in Crop Spatial Patterns: Current Status, Progress and Future Directions
by Pengnan Xiao, Peng Qian, Jie Xu and Mengyao Lu
Sustainability 2022, 14(7), 4104; https://doi.org/10.3390/su14074104 - 30 Mar 2022
Cited by 4 | Viewed by 2850
Abstract
The crop spatial pattern (CSP) is the spatial expression of the planting structure, maturity and planting pattern of crops in a region or production unit. It reflects the situation of human agricultural production using agricultural production resources, and is very important for human [...] Read more.
The crop spatial pattern (CSP) is the spatial expression of the planting structure, maturity and planting pattern of crops in a region or production unit. It reflects the situation of human agricultural production using agricultural production resources, and is very important for human survival and development. Based on 5356 publications collected from the Web of Science Core CollectionTM (WoS), this paper’s aim is to illustrate a comprehensive run-through and visualization of the subject of CSP. A time series evolution diagram of hot topics and the evolution of research hotspots are discussed in detail. Then, remote sensing monitoring methods of the crop planting area, multiple cropping, crop planting patterns and the mechanisms of crop spatial patterns are summarized, respectively. In the discussion, we focus on three important issues, namely, the remote sensing cloud platform, the changes in characteristics of the crop spatial pattern and the simulation of the crop spatial pattern. The main objective of the paper is to assist research workers interested in the area of CSP in determining potential research gaps and hotspots. Full article
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture)
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