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.

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%.

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.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Will Sustainable Food Sovereignty Research Be Sustainable in the Future?

The article proposes two agricultural paradigms to address global food production sustainability. First, precision agroecology may unite production-oriented and ecological agriculture, but it offers distinct solutions based on data, innovation, and decision-analysis technologies. The author demonstrates how precision technology and agroecological principles can transform agriculture by 1) minimizing inputs with optimization prescriptions, 2) replacing self-sustaining inputs with location variable rate technology, 3) integrating functional ecosystems into agroecosystems with exact preservation technology, 4) hooking up farmers and consumers via value-based food ecosystems, and 5) establishing equitable agroecology. Hence, precision agroecology provides a rare opportunity to integrate indigenous practices and contemporary technologies to revolutionize farming practices. Precision agroecology can tackle agriculture’s most serious sustainability issues in a world in flux.

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.

Advanced Irrigation Engineering： Precision and Precise

Irrigation advances in precision irrigation （PI） or site specific irrigation （SSI） have been considerable in research; however, commercialization lags. SSI/PI has applications when soil texture variability affects soil water holding capacity or when crop yield or biotic stresses （insects or diseases） are spatially variable. SSI/PI uses variable rate application technologies, mainly with center-pivots or lateral-move or linear irrigation machines, to match crop needs or soil water holding constraints. Variable rate applications are achieved by variable nozzle flow rates, pulsing nozzle flows, or multiple nozzles on separate submains. Newer center pivot and linear machines are controlled by on-board microprocessor systems that can be integrated with supervisory control and data acquisition controllers for both communication and control of the variable rate application for specific sets of nozzles or individual nozzles for management zones. Communication for center pivot or linear controllers typically uses radio telemetry, wireless interact links, or cellular telephones. Precision irrigation has limited utility without precise irrigation scheduling （temporally and spatially）. Plant or soil sensors are used to initiate or complete an irrigation event. Automated weather stations provide site information for determining the irrigation requirement using crop models or simpler reference evapotranspiration （ET）, data to be used with crop coefficients. Remote sensing is being used to measure crop water status or crop development from spectral reflectance. Near-surface remote sensing with sensors mounted on moving irrigation systems provide critical spatial integration from point weather networks and feedback on crop ET and irrigation controls in advanced automated systems for SSI/PI.

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.

GIS-Supported Precision Agricultural Fertilization Management at Small Scale

Precision agricultural fertilization technique at small scale is the key for regional cultivated land protection and management. Taking Dingzhuang Town (located in Guangrao County, Shandong Province) as a case study, this article explores the precision agricultural fertilization technique at small scale. First, three main cropping systems are identified via field investigation, namely "wheat–maize–soybean", "garlic–maize" and "cotton". Then, application amount of fertilizer N, P and K under the three cropping systems is calculated on the parameters acquired through field experiment, using nutrient balance approach on the support of geographic information system technique. The results indicate that, precision agricultural fertilization technique at small scale take on different characteristics compared with that at larger scale. The spatial distribution of soil nutrients in Dingzhuang Town is out of equilibrium, and huge amount fertilizer is needed to satisfy local agricultural production. There is huge distinction of fertilizer ap- plication amount under different cropping systems. The demand of fertilizer amount under "wheat–maize–soybean" system is larger than that under "garlic–maize" and "cotton" systems. This study can provide theories and principles for regional precision agricul- tural fertilization management.

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.

Probability and Statistical Error of Soil Sampling on Patches Assimilated during Delineation of Precision Farming Management Zones

When precision fanning management zones （MZs） are delineated in an agricultural field for precision nutrient management, unsupervised classification and cluster analysis procedures using remote sensing image analysis software are performed. These unsupervised classification and cluster analysis procedures are performed on the basis of the assumption that grouping of data points into naturally occurring clusters reduces within zone variability. The problem is that, there are small patches of different soil types within each management zone that are regarded as insignificant by the farmer, and are assimilated within larger MZs. These will consequently make soils within a management zone to be inhomogeneous. The objective of this study was to determine the probability of soil sampling occurrences on patches assimilated during delineation of MZs after a cluster analysis was performed. The study was conducted on a 5.0 ha （25°05′34.46″ S and 28°18′30.01″ E） and a 24.4 ha （23°59′04.61″ S and 28°52′29.43″ E） fields in the Waterberg District of the Limpopo Province in South Africa. A bare-soil high resolution Quickbird satellite imagery of a conventionally tilled agricultural field was used to develop MZs in the field. Soils were sampled using systematic unaligned sampling on a 35.0 m and 30.0 m grids for the 24.4 ha and 5.0 ha fields, respectively. Probabilities were calculated based on percentage area assimilated during the cluster analysis procedure that was performed using remote sensing image analysis software. The results indicated that in the 24.4 ha field there were 2.5 ha patches of high and medium zones that were assimilated within the low zone, and thus making low zones non-homogeneous. After cluster analysis and assimilation of patches, the low zone in the 24.4 ha field increased by 45.5% （2.5 ha） while the high zone was 16.4% （2.4 ha） smaller in size. In the smaller field of 5.0 ha, the high zone, which was originally 3.20 ha, lost 0.37 ha （11.6%）, which was assimilated in either low or medium zone. The study indicates that unequal probability proportional to size sampling could be used to minimize error when sampling across precision farming MZs because typically the low, medium and high MZs are not of equal size and do not contribute equally towards the mean values of soil samples.