Application of the Electrical Resistivity Method in Precision Agriculture of Coffee Cultivation, in the Kabiri Area, Ícolo e Bengo Township, Luanda, Angola

The electrical resistivity method is a geophysical tool used to characterize the subsoil and can provide an important information for precision agriculture. The lack of knowledge about agronomic properties of the soil tends to affect the agricultural coffee production system. Therefore, research related to geoelectrical properties of soil such as resistivity for characterization the region of the study for coffee cultivation purposes can improve and optimize the production. This resistivity method allows to investigate the subsurface through different techniques: 1D vertical electrical sounding and electrical imaging. The acquisition of data using these techniques permitted the creation of 2D resistivity cross section from the study area. The geoelectrical data was acquired by using a resistivity meter equipment and was processed in different softwares. The results of the geoelectrical characterization from 1D resistivity model and 2D resistivity electrical sections show that in the study area of Kabiri, there are 8 varieties of geoelectrical layers with different resistivity or conductivity. Near survey in the study area, the lowest resistivity is around 0.322 Ω·m, while the highest is about 92.1 Ω·m. These values illustrated where is possible to plant coffee for suggestion of specific fertilization plan for some area to improve the cultivation.

Coping with the Impact of Climate Change: A Dive into Precision Agriculture in the United States

With the continued increase in the number of people that are food insecure globally, which could be increasing because of the ongoing Ukraine-Russia war, leading to reduction in international agribusinesses, coupled with drastic climate change exacerbating the problem of food insecurity, there is a constant need to come up with innovative approaches to solve this global issue. In this article, we articulated how precision agriculture can be a tool for ensuring food security in the United States. This study aims to reiterate the significance of precision agriculture in solving global food insecurity.

A Framework for Introducing Precision Agriculture Technologies in Egypt

Precision Agriculture(PA)has been used in many countries and serving the agricultural sectors.The use of PA solutions intervened with many agricultural businesses and supported decision making using data analytics.Precision Agriculture depends on weather,soil,plants,and water information that are essential for farming.Precision Agriculture depends on the use of several technologies such as image sensors,vision machines,drones,robots,machine learning,and artificial intelligence.The use of Precision Agriculture Technologies(PAT)depends on integration between devices,sensors,and systems to ensure the proper implementation of activities.This paper is generated from research on the applicability of PA in in Egypt that ended with a proposed framework for proper implementation of it.The conducted research depended on a survey,focus group discussions,and an online questionnaire that reached 271 respondents from 19 Egyptian governorates.The framework has been developed to enhance the role of an initiative leader to promote PAT through collaboration with other stakeholders in the agricultural sector.The proposed framework can be used by governmental,non-governmental entities,universities and private sector institutions and could be used at countries facing issues with land fragmentation,limited access to information,limited access to agricultural extension services,and increase in agricultural input’s prices.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

Clustered Wireless Sensor Network in Precision Agriculture via Graph Theory

Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can produce crops with a minimum amount of water and fertilizer.Even though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability.Towards such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efficientflow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance.Thefindings of blocks 1 and 2 are assessed.Each signal takes just a little amount of energy to communicate,according to the performance.It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs.However,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN.Based on the minimum edgefixed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control stations.The edge-fixed geodetic cluster makes the network fast,efficient and reliable.Moreover,it also solves routing and congestion problems.

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

Pesticide Deinsectization System in Precision Agriculture Based on ARM-Linux

With precision agriculture as the base line, using embedded system as technical support, a set of ideas is proposed for solving the serious pesticide poisoning problem, including farmland information collection, experts database analysis and variable pesticide spraying, etc.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

DESIGN OF FARMLAND GIS FOR PRECISION AGRICULTURE

Precision Agriculture, also known as Precision Farming, or Prescription Farming, is a modern agriculture technology system, which brings ' precision' into agriculture system. All concepts of Precision Agriculture are established on the collection and management of variable cropland information. As the tool of collecting, managing and analyzing spatial data, GIS is the key technology of integrated Precision Agriculture system. This article puts forward the concept of Farmland GIS and designs Farmland GIS into five modules, and specifies the functions of the each module, which builds the foundation for practical development of the software. The study and development of Farmland GIS will propel the spreading of Precision Agriculture technology in China.

Delineation and Scale Effect of Precision Agriculture Management Zones Using Yield Monitor Data Over Four Years

In this study, precision agriculture management zones were delineated using yield data over four years from the combine harvester equipped with yield monitor and DGPS receiver. Relative yields measured during each year were interpolated to 4 m2 grid size using ordinary kriging. The resultant interpolated yield maps were averaged across years to create a map of the mean relative yield, which was then used for cluster analysis. The mean yield map of post-classification was processed by applying majority filtering with window sizes that were equivalent to the grid sizes of 12, 20, 28, 36, 44, 52 and 60 m. The scale effect of management zones was evaluated using relative variance reduction, test of significant differences of the means of yield zones, spatial fragmentation, and spatial agreement. The results showed that the post-classification majority filtering （PCMF） eliminated lots of isolated cells or patches caused by random variation while preserving yield means, high variance reduction, general yield patterns, and high spatial agreement. The zoned result can be used as yield goal map for preplant or in-season fertilizer recommendation in precision agriculture.

Biosynthesized metallic nanoparticles as fertilizers:An emerging precision agriculture strategy

Nanofertilizers increase efficiency and sustainability of agricultural crop production.Due to their nanosize properties,they have been shown to increase productivity through target delivery or slow release of nutrients,thereby limiting the rate of fertilizer application required.Nanofertilizers can be synthesized via different approaches ranging from physical and chemical to green(biological)synthesis.The green approach is preferable because it makes use of less chemicals,thereby producing less chemical contamination and it is safer in comparison to physicochemical approaches.Hence,discussion on the use of green synthesized nanoparticles as nanofertilizers is pertinent for a sustainable approach in agriculture.This review discusses recent developments and applications of biologically synthesized metallic nanoparticles that can also be used as nanofertilizers,as well as their uptake mechanisms for plant growth.Toxicity concerns of nanoparticle applications in agriculture are also discussed.

Context Aware Wireless Sensor Network Suitable for Precision Agriculture

Using the Wireless Sensor Networks WSNs in a wide variety of applications is currently considered one of the most challenging solutions. For instance, this technology has evolved the agriculture field, with the precision agriculture challenge. In fact, the cost of sensors and communication infrastructure continuously trend down as long as the technological advances. So, more growers dare to implement WSN for their crops. This technology has drawn substantial interests by improving agriculture productivity. The idea consists of deploying a number of sensors in a given agricultural parcel in order to monitor the land and crop conditions. These readings help the farmer to make the right inputs at the right moment. In this paper, we propose a complete solution for gathering different type of data from variable fields of a large agricultural parcel. In fact, with the in-field variability, adopting a unique data gathering solution for all kinds of fields reveals an inconvenient approach. Besides, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, our approach deals with a context aware data gathering strategy. In other words, depending on a defined context for the monitored field, the data collector will decide the data gathering strategy to follow. We prove that this approach improves considerably the lifetime of the application.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

The Issue of Precision Agriculture in International Trade： An Analysis in Terms of Subsidies

At the present time, world agriculture is influenced by a set of new technologies grouped under the generic name of precision agriculture （PA）. Based on a study of the cotton sector, this article examines the effects of adopting PA with regard to international trade. We examine whether PA can contribute to the further destabilization of the terms of trade between countries in Central and West Africa （CWA） and Northern countries. We show that PA can be used by Northern countries at the expense of CWA, since it is used to implement strategic commercial policies based on subsidies. These policies are made more credible by the fact that international authorities cannot easily condemn them.

Development of sensor systems for precision agriculture in cotton

Precision agriculture(PA)is an information-based technology,using detailed information within an agricultural field to optimize production inputs on a spatially variable basis,maximize farm profit,and minimize environmental impact.Information collection and processing plays a very important role in PA.In recent years PA technologies have been gradually adopted in cotton production.Several sensor systems for PA were developed and field-evaluated in cotton,including a plant height measurement system(PHMS),the Mississippi cotton yield monitor(MCYM),and cotton fiber quality mapping.The PHMS used an ultrasonic sensor to scan the plant canopy and determine plant height in real time in situ.A plant height map was generated with the data collected with the PHMS.Cotton plant height showed a close relationship with yield(R2=0.63)and leaf-nitrogen content(R2=0.48).The MCYM was developed for cotton yield mapping.A patented mass-flow sensor technology was employed in the MCYM.The sensor measured optical reflectance of cotton particles passing through the sensor and used the measured reflectance to determine cotton-mass flow rates.Field tests indicated that the MCYM could measure cotton yield with an average error less than 5%,and it was easy to install and maintain.The cotton fiber-quality mapping research involved a wireless cotton module-tracking system(WCMTS)and a cotton fiber quality mapping system(CFQMS).The WCMTS was based on the concept that a cotton fiber-quality map could be generated with spatial information collected by the system during harvesting coupled with fiber quality information available in cotton classing offices.The WCMTS was constructed and tested,and it operated according to design,with module-level fiber-quality maps easily made from the collected data.The CFQMS was designed and fabricated to perform real-time measurement of cotton fiber quality as the cotton is harvested in the field.Test results indicated that the sensor was capable of accurately estimating fiber micronaire in lint cotton(R2=0.99),but estimating fiber quality in seed cotton was more difficult.Cotton fiber quality maps can be used with cotton yield maps for developing field profit maps and optimizing production inputs.

Agricultural experts’attitude towards precision agriculture:Evidence from Guilan Agricultural Organization,Northern Iran

Identifying factors that influence the attitudes of agricultural experts regarding precision agriculture plays an important role in developing,promoting and establishing precision agriculture.The aim of this study was to identify factors affecting the attitudes of agricultural experts regarding the implementation of precision agriculture.A descriptive research design was employed as the research method.A research-made questionnaire was used to examine the agricultural experts’attitude toward precision agriculture.Internal consistency was demonstrated with a coefficient alpha of 0.87,and the content and face validity of the instrument was confirmed by a panel of experts.The results show that technical,economic and accessibility factors accounted for 55%of the changes in attitudes towards precision agriculture.The findings revealed that there were no significant differences between participants in terms of gender,field of study,extension education,age,experience,organizational position and attitudes,while education levels had a significant effect on the respondent’s attitudes.

Possibilities and concerns of implementing precision agriculture technologies on small farms in Slovenia

Precision agriculture(PA)through the use and utilization of innovative technologies is a concept in agricultural management that enables long-term efficiency gains,control of unforeseen changes,and a reduction of negative impacts on the environment.However,there are even more reasons and benefits to using precision agriculture technologies(PATs)on farms,but the actual use on small farms is often questionable.The main objective of this research was to evaluate and analyze the current state of PA and its potential on a set of small farms.In addition,a comparison was made between small farms located in less favored areas(LFAs)and more favored areas(MFAs)to find if specific characteristics of the surrounding environment affect the(non-)implementation of these technologies by farm owners,with respect to the given regional possibilities.The result shows that 57.5%of respondents on these farms have never implemented PATs before and 20%are beginners in their respective fields.It was found that there were no statistically significant differences in the integration between fewer LFAs and MFAs technologies and their use in this study.The majority of respondents believe that the main changes need to occur on the level of politics.The results show that the level of cost or initial investment is the main reason and the main obstacle in the implementation of PATs on the surveyed farms.

A tree counting algorithm for precision agriculture tasks

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.

Prediction of environment variables in precision agriculture using a sparse model as data fusion strategy

Precision agriculture seeks to optimize production processes by monitoring and analyzingenvironmental variables. For example, establishing farming actions on the crop requiresanalyzing variables such as temperature, ambient humidity, soil moisture, solar irradiance,and Rainfall. Although these signals might contain valuable information, it is vital to mixup the monitored signals and analyze them as a whole to provide more accurate information than analyzing the signals separately. Unfortunately, monitoring all these variablesresults in high costs. Hence it is necessary to establish an appropriate method that allowsthe infer variables behavior without the direct measurement of all of them.This paper introduces a multi-sensor data fusion technique, based on a sparse representation, to find the most straightforward and complete linear equation to predict and understand a particular variable behavior based on other monitored environmental variablesmeasurements. Moreover, this approach aims to provide an interpretable model that allowsunderstanding how these variables are combined to achieve such results. The fusion strategy explained in this manuscript follows a four-step process that includes 1. data cleaning,2. redundant variable detection, 3. dictionary generation, and 4. sparse regression. Thealgorithm requires a target variable and two highly correlated signals. It is essential to pointout that the developed method has no restrictions to specific variables. Consequently, it ispossible to replicate this method for the semiautomatic prediction of multiple critical environmental variables.As a case study, this work used the SML2010 data set of the UCI machine learning repository to predicted the humidity’s derivative trend function with an error rate lower than 17%and a mean absolute error lower than 6%. The experiment results show that even thoughsparse model predictions might not be the most accurate compared to those of linearregression (LR), support vector machine (SVM), and extreme learning machine (ELM) sinceit is not a black-box model, it guarantees greater interpretability of the problem.