Adoption of Precision Agriculture by Detecting and Spraying Herbicide using UAV

Farmers are facing the VUCA environment (volatile, uncertain, complex and ambiguous) and data indicating the contribution of farming to India's GDP has come down from 52% to 18% between 1951 and 2018, which is alarming. At this juncture, developing countries like India, where over 70% of the rural people depend upon the agriculture fields, adoption of disruptive technology (creative destruction) becomes the need of the hour, to enhance the crop yield and quality. Weeds are one of the major issues which severely affect the crop output. Unmanned Aerial Vehicle (UAV) or drone is recommended, to address the problem. Globally, the market for agriculture drones to move from $1.3 billion to $ 6.52 billion by 2026. Globally agriculture is the second largest industry after construction in terms of drone adoption. But Indian farmers have difficulty in adopting (or) procuring UAV's, as the size of their farm is small, income is very less. Other problems associated with the adoption of UAV include knowledge transfer and training to farmers, service support and maintenance cost. DaaS (Drone as a service) model is proposed, for rural areas. This paper aims to focus on weed management by providing a safer and cost-effective solution. By integrating technologies like visible light (VIS), near-infrared (NIR) light on an Unmanned Ariel Vehicle along with a precise sprayer and a weed detection system backed up by a lithium-ion battery (for longer flight duration), can help the process of spraying weedicide efficiently. The accuracy of the tested model is 92.6% for far away detection module and 95.4 for close range detection. UAV's with sprayer protects the farmer and consumers from odour and side effects.

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Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

Planta Daninha ◽

10.1590/s0100-83582012000200024 ◽

2012 ◽

Vol 30 (2) ◽

pp. 437-447 ◽

Cited By ~ 5

Author(s):

A. Merotto JR. ◽

C. Bredemeier ◽

R.A. Vidal ◽

I.C.G.R. Goulart ◽

E.D. Bortoli ◽

...

Keyword(s):

Weed Control ◽

Precision Agriculture ◽

Weed Management ◽

Dry Matter ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Weed Interference ◽

Reflectance Indices ◽

The Relationship

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

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CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-349-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 349-356 ◽

Cited By ~ 3

Author(s):

M. Hassanein ◽

M. Khedr ◽

N. El-Sheimy

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Weed Management ◽

Low Cost ◽

Color Space ◽

Orientation Angle ◽

Weed Detection ◽

Agriculture Industry ◽

Rgb Images ◽

Scan Line

Abstract. Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.

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Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

Remote Sensing ◽

10.3390/rs13245182 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5182

Author(s):

Aaron Etienne ◽

Aanis Ahmad ◽

Varun Aggarwal ◽

Dharmendra Saraswat

Keyword(s):

Deep Learning ◽

Object Detection ◽

Weed Management ◽

Detection System ◽

Ground Level ◽

Training Image ◽

Field Conditions ◽

Herbicide Application ◽

Weed Detection ◽

Management Procedures

Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for targeted herbicide application within a field. However, a major challenge to developing a weed detection system is the requirement for a properly annotated database to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of 374 red, green, and blue (RGB) color images organized into monocot and dicot weed classes. The images were acquired from corn and soybean research plots located in north-central Indiana using an unmanned aerial system (UAS) flown at 30 and 10 m heights above ground level (AGL). A total of 25,560 individual weed instances were manually annotated. The annotated database consisted of four different subsets (Training Image Sets 1–4) to train the You Only Look Once version 3 (YOLOv3) deep learning model for five separate experiments. The best results were observed with Training Image Set 4, consisting of images acquired at 10 m AGL. For monocot and dicot weeds, respectively, an average precision (AP) score of 91.48 % and 86.13% was observed at a 25% IoU threshold (AP @ T = 0.25), as well as 63.37% and 45.13% at a 50% IoU threshold (AP @ T = 0.5). This research has demonstrated a need to develop large, annotated weed databases to evaluate deep learning models for weed identification under field conditions. It also affirms the findings of other limited research studies utilizing object detection for weed identification under field conditions.

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AN EFFICIENT WEED DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM FOR PRECISION AGRICULTURE APPLICATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-181-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 181-187 ◽

Cited By ~ 3

Author(s):

M. Hassanein ◽

N. El-Sheimy

Keyword(s):

Precision Agriculture ◽

Weed Management ◽

Low Cost ◽

Weed Detection ◽

Detection Techniques ◽

Management Procedure ◽

Satellite Platform ◽

Weed Plants ◽

Aerial Vehicle ◽

The Cost

Abstract. The use of Unmanned Aerial Vehicle (UAV) imagery systems for Precision Agriculture (PA) applications drew a lot of attention through the last decade. UAV as a platform for an imagery sensor is providing a major advantage as it can provide high spatial resolution images compared to satellite platform. Moreover, it provides the user with the ability to collect the needed images at any time along with the ability to cover the agriculture fields faster than terrestrial platform. Therefore, such UAV imagery systems are capable to fit the gap between aerial and terrestrial Remote Sensing. One of the important PA applications that using UAV imagery system for it showed great potentials is weed management and more specifically the weed detection step. The current weed management procedure depends on spraying the whole agriculture field with chemical herbicides to execute any weed plants in the field. Although such procedure seems to be effective, it has huge effect on the surrounding environment due to the excessive use of the chemical, especially that weed plants don’t cover the whole field. Usually weed plants spread through only few spots of the field. Therefore, different efforts were introduced to develop weed detection techniques using UAV imagery systems. Though the different advantages of the UAV imagery systems, they systems didn’t draw the users interest due to many limitations including the cost of the system. Therefore, the proposed paper introduces a new weed detection methodology from RGB images acquired by low-cost UAV imagery system. The proposed methodology adopts detecting the high-density vegetation spots as indication for weed patches spots. The achieved results showed the potential of the proposed methodology to use low-cost UAV imagery system equipped with low-cost RGB imagery sensor for detecting weed patches in different cropped agriculture fields even from different flight height as 20, 40, 80, and 120 meters.

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Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽

10.3390/s21093052 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3052

Author(s):

Mas Ira Syafila Mohd Hilmi Tan ◽

Mohd Faizal Jamlos ◽

Ahmad Fairuz Omar ◽

Fatimah Dzaharudin ◽

Suramate Chalermwisutkul ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Early Detection ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Predictive Analytics ◽

Detection System ◽

Imaging Techniques ◽

Ganoderma Boninense ◽

Machine Learning Classification ◽

Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

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Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽

10.3390/agronomy11040646 ◽

2021 ◽

Vol 11 (4) ◽

pp. 646

Author(s):

Bini Darwin ◽

Pamela Dharmaraj ◽

Shajin Prince ◽

Daniela Elena Popescu ◽

Duraisamy Jude Hemanth

Keyword(s):

Deep Learning ◽

Crop Yield ◽

Precision Agriculture ◽

Soil Parameters ◽

Learning Models ◽

Weed Detection ◽

Agricultural Industry ◽

Agricultural Robots ◽

Detection Techniques ◽

Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

Malaria Journal ◽

10.1186/s12936-021-03759-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Michelle C. Stanton ◽

Patrick Kalonde ◽

Kennedy Zembere ◽

Remy Hoek Spaans ◽

Christopher M. Jones

Keyword(s):

Image Analysis ◽

Malaria Control ◽

Rural Areas ◽

Near Infrared ◽

Aquatic Vegetation ◽

Temporal Trends ◽

Scale Up ◽

Larval Habitat ◽

Habitat Characteristics ◽

Rural Environments

Abstract Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.

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Techno-Economic Analysis of Biomass Energy Utilization through Gasification Technology for Sustainable Energy Production and Economic Development in Nigeria

Journal of Energy ◽

10.1155/2018/4860252 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Gbeminiyi M. Sobamowo ◽

Sunday J. Ojolo

Keyword(s):

Economic Analysis ◽

Rural Areas ◽

Financial Incentives ◽

Capital Investment ◽

Biomass Gasification ◽

Biomass Energy ◽

Energy Utilization ◽

Maintenance Cost ◽

Selling Price ◽

Gasification Technology

Nigeria has not been able to provide enough electric power to her about 200 million people. The last effort by the federal government to generate 6000 MW power by the end of 2009 failed. Even with the available less than 6000 MW of electricity generated in the country, only about 40% of the population have access to the electricity from the National Grid, out of which, urban centers have more than 80% accessibility while rural areas, which constitute about 70% of the total population, have less than 20% of accessibility to electricity. This paper addresses the possibility of meeting the energy demand in Nigeria through biomass gasification technology. The techno-economic analysis of biomass energy is demonstrated and the advantages of the biomass gasification technology are presented. Following the technical analysis, Nigeria is projected to have total potential of biomass of about 5.5 EJ in 2020 which has been forecast to increase to about 29.8 EJ by 2050. Based on a planned selling price of $0.727/kWh, the net present value of the project was found to be positive, the cost benefit ratio is greater than 1, and the payback period of the project is 10.14 years. These economic indicators established the economic viability of the project at the given cost. However, economic analysis shows a selling price of $0.727/kWh. Therefore, the capital investment cost, operation and maintenance cost, and fuel cost can be reduced through the development of the gasification system using local materials, purposeful and efficient plantation of biomass for the energy generation, giving out of financial incentives by the government to the investors, and locating the power plant very close to the source of feedstock generation.

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A Long-range Context-aware Platform Design For Rural Monitoring With IoT In Precision Agriculture

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.2.3821 ◽

2020 ◽

Vol 15 (2) ◽

Cited By ~ 1

Author(s):

Deniz TAŞKIN ◽

Selçuk YAZAR

Keyword(s):

Long Range ◽

Precision Agriculture ◽

Rural Areas ◽

Smart Cities ◽

Soil Surface ◽

Area Network ◽

Context Aware ◽

Wide Area Network ◽

Performance Measurements ◽

Iot Applications

The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measurements of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed.

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Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

10.31223/x5ww4z ◽

2021 ◽

Author(s):

Joanna Joiner ◽

Zachary Fasnacht ◽

Bo-Cai Gao ◽

Wenhan Qin

Keyword(s):

Spatial Resolution ◽

Satellite Data ◽

Precision Agriculture ◽

Land Surface ◽

Near Infrared ◽

Infrared Imaging ◽

Space Station ◽

Water Quality Assessment ◽

Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

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