Review
Chemistry, Multidisciplinary
Heyu Yin, Yunteng Cao, Benedetto Marelli, Xiangqun Zeng, Andrew J. Mason, Changyong Cao
Summary: Soil sensors and plant wearables are crucial in smart agriculture, monitoring soil signals to optimize crop growth and yields. This review covers important soil sensors, their technologies, designs, and performance, as well as discusses emerging technologies and challenges in precision agriculture.
ADVANCED MATERIALS
(2021)
Article
Agriculture, Multidisciplinary
Whoi Cho, Abby ShalekBriski, B. Wade Brorsen, Davood Poursina
Summary: Precision agriculture requires combining multiple measurement methods. This study proposes a method that utilizes Bayesian Kriging to estimate the joint spatial distribution of measurements and uses Bayesian Decision Theory and a grid search procedure to determine the economic optimum of these measurements. Comparison with other methods demonstrates the accuracy and economic value of this approach in soil mapping.
PRECISION AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Justine M. Nyaga, Cecilia M. Onyango, Johanna Wetterlind, Mats Soderstrom
Summary: Precision agriculture has great potential for growth in sub-Saharan Africa, but it faces challenges in terms of socio-economic factors and technology. Most research has been conducted in countries like South Africa, Nigeria, and Kenya, primarily on small farms. Collaboration between researchers from inside and outside Africa has played a significant role in advancing precision agriculture in the region.
PRECISION AGRICULTURE
(2021)
Article
Chemistry, Multidisciplinary
Aristotelis C. Tagarakis, Dimitrios Kateris, Remigio Berruto, Dionysis Bochtis
Summary: This study presents a low-cost, low-power wireless sensor network system designed for agricultural environments, featuring a star topology, solar energy harvesting panels, and a virtual coordinator device. The system demonstrated satisfactory operation in laboratory and real field environments, showing potential as a viable option for monitoring environmental, soil, and crop parameters.
APPLIED SCIENCES-BASEL
(2021)
Review
Agronomy
Mohammad Nishat Akhtar, Abdurrahman Javid Shaikh, Ambareen Khan, Habib Awais, Elmi Abu Bakar, Abdul Rahim Othman
Summary: This article highlights the shift towards data-driven agriculture with the implementation of IoT, emphasizing the necessity for developing countries to incorporate IoT due to their dependence on the agricultural sector. The review provides an overview of advanced technologies used in precision agriculture and stresses the importance of optimized data processing methods derived from cloud computing and the significance of edge computing.
Article
Engineering, Electrical & Electronic
Tanmay Anand, Soumendu Sinha, Murari Mandal, Vinay Chamola, Fei Richard Yu
Summary: Aerial inspection of agricultural regions provides crucial information to safeguard efficient farming, while monitoring farmland anomalies is essential for increasing agricultural technology efficiency and developing AI-assisted farming models. The proposal of the deep learning framework AgriSegNet contributes to automated detection of farmland anomalies and enhancing precision farming techniques.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Pankaj Kumar Kashyap, Sushil Kumar, Ankita Jaiswal, Mukesh Prasad, Amir H. Gandomi
Summary: Precision agriculture has gained attention due to growing demands for food and water, leading to the need for more efficient farming methods. This paper introduces an intelligent irrigation system using deep learning to predict soil moisture content and optimize water usage, outperforming current models in experimental farming.
IEEE SENSORS JOURNAL
(2021)
Article
Agronomy
Gaganpreet Singh Hundal, Chad Matthew Laux, Dennis Buckmaster, Mathias J. Sutton, Michael Langemeier
Summary: The production of row crops in the Midwestern (Indiana) region of the US is facing environmental and economic sustainability issues. The low adoption rate of IoT-based precision agriculture technologies and the barriers to their adoption, including cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing, and data interoperability, are described in the literature. This study explores and understands decision-making variables related to these barriers through focus group interviews with subject matter experts in IoT-based precision agriculture practices.
Article
Agriculture, Multidisciplinary
Gustavo Willam Pereira, Domingos Sarvio Magalhaes Valente, Daniel Marcal de Queiroz, Nerilson Terra Santos, Elpidio Inacio Fernandes-Filho
Summary: Kriging is the optimal interpolator for precision agriculture, but it requires a high number of sampling points for accurate map generation. Machine learning techniques have shown potential in generating maps with fewer sampling points. In this study, a Support Vector Machine (SVM) algorithm was implemented and compared to IDW and Ordinary Kriging (OK). Results showed that OK outperformed IDW and the ML method when Moran's I values were significant and higher than 0.67. However, the ML method performed better than IDW and OK in situations with low density of points and low degrees of spatial autocorrelation.
PRECISION AGRICULTURE
(2022)
Article
Chemistry, Analytical
Karel Pavelka, Paulina Raeva, Karel Pavelka
Summary: The main goals of this paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements, as well as to present an enhanced workflow for processing multitemporal image data. The researchers found a high correlation between the aerial and field data and introduced image processing steps and an enhanced photogrammetric workflow to reduce processing time for both experts and non-professionals.
Article
Engineering, Electrical & Electronic
Marios Sophocleous, Andreas Karkotis, Julius Georgiou
Summary: This study introduces a versatile, low-cost sensing node for direct soil quality monitoring with high accuracy and linearity, supporting various communication technologies for IoT applications. The system is flexible and can accommodate different sensors operating under the same principles, making modifications through software during sensor calibration.
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
(2021)
Review
Plant Sciences
Chen Sun, Jing Zhou, Yuchi Ma, Yijia Xu, Bin Pan, Zhou Zhang
Summary: Potato is a significant food crop globally, and precision agriculture is recognized as a solution to improve agricultural returns and reduce environmental impact. Traditional methods of crop and field characterization require a large input in labor, time, and cost, but recent developments in remote sensing technologies have facilitated the process. This review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization and provides a selective list for those interested in applying these technologies for precision agriculture.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Biophysics
Steven R. Schultze, Murdoch N. Campbell, Samantha Walley, Katie Pfeiffer, Bryan Wilkins
Summary: The field of precision agriculture has introduced the concept of big data to farming by utilizing sensor technology, allowing growers to make more efficient management decisions. This study focused on exploring temperature differences at a sub-field level and found that temperatures can vary significantly within the same grove at the same moment. Extreme cold events were found to be non-uniform within the grove, impacting tree health and fruit production.
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
(2021)
Article
Computer Science, Information Systems
Sayan Kumar Roy, Debashis De
Summary: This article introduces a unique system in the field of IoT agriculture applications, using genetic algorithm to predict rainfall and recommend watering. The system verifies predictions by monitoring soil moisture levels to enhance system efficiency.
INTERNET OF THINGS
(2022)
Article
Computer Science, Information Systems
Showkat Ahmad Bhat, Nen-Fu Huang
Summary: This article discusses the latest applications of Big Data in smart agriculture, including data creation methods, technology accessibility, device accessibility, software tools, data analytic methods, and appropriate applications of big data in precision agriculture. It also mentions some challenges faced in the widespread implementation of big data technology in agriculture.
Article
Agronomy
Tatiana Fernanda Canata, Mauricio Martello, Leonardo Felipe Maldaner, Jadir de Souza Moreira, Jose Paulo Molin
Summary: The adoption of LiDAR technology for spatial analysis of sugarcane fields revealed high spatial variability in plant height, demonstrating the potential of 3D sensing data for crop assessment and production level indication.
Article
Agronomy
Leonardo Felipe Maldaner, Tatiana Fernanda Canata, Jose Paulo Molin
Summary: By installing a hydraulic oil pressure sensor in the chopper of a sugarcane harvester, this study aimed to evaluate the accuracy of sugarcane mass prediction. The results suggest that increasing the data collection frequency by the harvester can improve the spatial variability detection of sugarcane yield at the field level without the need for empirical models or sensor calibration.
Article
Agriculture, Multidisciplinary
Leonardo Felipe Maldaner, Jose Paulo Molin, Tatiana Fernanda Canata, Mauricio Martello
Summary: The study aimed to test an alternative system for detecting sugarcane plants within rows and compare the accuracy of different machine learning models. By using a combination of photoelectric sensor, ultrasonic sensor, and encoder, the research found that the approach with two sensors and the decision tree model had the best precision in plant detection.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Soil Science
Tiago R. Tavares, Abdul M. Mouazen, Lidiane C. Nunes, Felipe R. dos Santos, Fabio L. Melquiades, Thainara R. da Silva, Francisco J. Krug, Jose P. Molin
Summary: The study compared different modelling methods of LIBS data and found the iSPA-PLS method to be the most effective for predicting key fertility attributes in Brazilian tropical soils, providing an efficient and accurate modelling approach. The research discovered the potential value of LIBS technique for predicting fertility attributes in tropical soils, but further investigation is needed to reduce sample preparation procedures.
SOIL & TILLAGE RESEARCH
(2022)
Article
Agriculture, Multidisciplinary
Helizani Couto Bazame, Jose Paulo Molin, Daniel Althoff, Mauricio Martello, Lucas De Paula Corredo
Summary: This study implemented a computer vision algorithm to quantify the number of coffee fruits and create yield maps. The results showed that this method effectively explained the factors influencing yield variations and had the advantages of low cost and independence from specific coffee harvester brands.
PRECISION AGRICULTURE
(2022)
Article
Agronomy
Mauricio Martello, Jose Paulo Molin, Helizani Couto Bazame, Tiago Rodrigues Tavares, Leonardo Felipe Maldaner
Summary: This study evaluated the potential of active optical sensors (AOS) to map the spatial and temporal variability of coffee crop yields and provided guidelines for data acquisition. The results showed that different faces of the same coffee plant have different correlations with yield. Vegetation indices measured at the beginning of the coffee cycle have a positive correlation with the yield of that year, but the correlation becomes negative after the start of the rainy season. Additionally, the vegetation index acquired at a specific time has an inverted relationship with the yield of that year and the following (or previous) year due to the biennial nature of coffee production.
Review
Agronomy
Mauricio Roberto Cherubin, Junior Melo Damian, Tiago Rodrigues Tavares, Rodrigo Goncalves Trevisan, Andre Freitas Colaco, Mateus Tonini Eitelwein, Mauricio Martello, Ricardo Yassushi Inamasu, Osmar Henrique de Castro Pias, Jose Paulo Molin
Summary: Precision agriculture in Brazil has shown significant growth in the past 25 years, with an increasing number and quality of publications, research group interactions, and international collaborations. Soil and plant management are the main focus areas, but research has expanded to include the use of sensors, remote sensing technologies, and decision support tools. A large portion of Brazilian precision agriculture research involves evaluating and adapting imported technologies, but there is potential for future research in digitally based decision support systems, on-farm experimentation, and machine learning approaches.
Article
Agricultural Engineering
Joao V. M. Nicoletti, Marcello R. A. Franchi, Anamari V. de A. Motomiya, Wagner R. Motomiya, Jose P. Molin
Summary: This study assessed the performance of three soil samplers in different management systems. Significant differences were observed in the quality of samples for certain attributes. The hydraulic sampler demonstrated the highest operational efficiency.
REVISTA BRASILEIRA DE ENGENHARIA AGRICOLA E AMBIENTAL
(2022)
Article
Agricultural Engineering
Ricardo Canal Filho, Jose Paulo Molin, Marcelo Chan Fu Wei, Eudocio Rafael Otavio da Silva
Summary: Spatio-temporal local calibrations are required for accurate soil attribute prediction using online NIR spectra ML models.
Article
Agriculture, Multidisciplinary
Helizani Couto Bazame, Jose Paulo Molin, Daniel Althoff, Mauricio Martello
Summary: This study proposes a computer vision system based on deep learning algorithms to detect and classify the maturation stage of coffee fruits. The YOLOv4 and YOLOv3 models showed promise in guiding coffee farmers' decision-making processes.
Article
Computer Science, Artificial Intelligence
Marcelo Chan Fu Wei, Ricardo Canal Filho, Tiago Rodrigues Tavares, Jose Paulo Molin, Afranio Marcio Correa Vieira
Summary: This study evaluated the predictive performance of two dimensionality reduction statistical models (PCR and lasso) for modeling soil spectral data without pretreatment techniques. The results showed that PCR and lasso achieved good performance in predicting soil attributes using raw spectral data. The comparison with literature results that employed pretreatment techniques indicated similar performance. However, there was no consensus on the best calibration approach, which seemed to be attribute and area specific.
Article
Agricultural Engineering
Mauricio Martello, Jose Paulo Molin, Marcelo Chan Fu Wei, Ricardo Canal Filho, Joao Vitor Moreira Nicoletti
Summary: Coffee production in Brazil is of high relevance, and this study aims to improve yield prediction models based on satellite images and yield data. The study identifies the best phenological stage for satellite image acquisition and shows that spectral bands and indexes like NDVI and GNDVI can accurately capture the spatial variability of coffee yield. The random forest model with spectral bands performs the best for yield quantification. These findings are important for precision agriculture management decisions.
Article
Remote Sensing
Mauricio Martello, Jose Paulo Molin, Graciele Angnes, Matheus Gabriel Acorsi
Summary: The study demonstrates the possibility of using RGB aerial images to obtain 3D information of coffee crops, including plant height and volume. The results show a correlation between plant height and yield data, providing insights into the spatial variability of coffee yield within the field.
Article
Horticulture
Mauricio Martello, Jose Paulo Molin, Helizani Couto Bazame
Summary: This study evaluates the quality of yield data obtained through a yield monitor onboard a coffee harvester and finds a high correlation with data collected using traditional measurement methods. Additionally, by collecting data over three consecutive seasons, the study identifies the internal variability of coffee yield and categorizes regions based on alternating yield patterns between years. The findings suggest that, in order to make effective management decisions, both spatial and biennial yield variability should be taken into account.
Article
Agriculture, Multidisciplinary
Leonardo Felipe Maldaner, Jose Paulo Molin, Mark Spekken
Summary: The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, in order to improve the accuracy and spatial variability characterization of interpolated data. The results showed that the developed filter process, which included global, anisotropic, and anisotropic local analysis, effectively decreased the nugget effect and significantly improved the spatial variability within the data sets. The methodology was tested using raw data sets of corn yield, soil electrical conductivity, and sensor vegetation index, and it successfully reduced RMSE and excluded local outliers.
