WEED DETECTION IN WHEAT CROP USING UAV for PRECISION AGRICULTURE

Mateen, Abdul

doi:10.21162/pakjas/19.8036

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…The hyperspectral data-cube consisted of 100 individual wavelengths captured in steps of 5nm [28]. An automated weed detection and crop monitoring system was proposed in a study conducted in 2019 that used several vegetative indices and object-based image analysis on multispectral images to capture weed patches in wheat fields [29]. An RBG sensor and a Tetracam NIR sensor were used for data acquisition in that experiment.…”

Section: Multispectral and Hyperspectral Sensorsmentioning

confidence: 99%

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

Shrestha,

Ojha,

Sharma

et al. 2024

JPA

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Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.

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Section: Multispectral and Hyperspectral Sensorsmentioning

confidence: 99%

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

Shrestha,

Ojha,

Sharma

et al. 2024

JPA

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“…Asides from phenotyping crops, drones are also used to detect weeds in order to determing the proper amount and distribution of pesticides. A study in Pakistan by [74] uses a DJI Phantom 4 with an RGB sensor TCS34725 and a NIR sensor 230218 applied object based image analysis (OBIA) algorithm to detect weed patches between and within wheat crop rows. Another study by [75] that took place in France proposed a method for weed detection using convolutional neural networks (CNNs), and utilised a DJI Phantom 3 Pro drone mounted with a 36-megapixel RGB camera on bean and spinach fields.…”

Section: B Uav-based Remote Sensingmentioning

confidence: 99%

From Satellite to UAV-Based Remote Sensing: A Review on Precision Agriculture

Phang,

Chiang,

Happonen

et al. 2023

IEEE Access

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The applications of remote sensing technologies to precision agriculture were reviewed. The different uses of conventional satellites and unmanned aerial vehicles (UAVs) for data collection were also examined. The ways in which the collected data have been used in terms of precision agriculture are laid out in further detail. Modern UAVs provide a range of advantages over conventional satellites in data collection and analysis. In addition, precision agriculture itself is a wide topic; and practitioners, researchers, and policy planners need to account for a complex value chain with diverse stakeholders. Thus, in order to further the discussion in a meaningful manner, the later sections of this article provide additional analysis on topics deemed crucial for the development of precision agriculture: a discussion on key technological limitations for scaling, a corresponding list of suggestions for the development of solutions, and a brief overview on the current social-economical landscape of the agriculture industry.INDEX TERMS Aerial mapping, precision agriculture, remote sensing, unmanned aerial vehicles.

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“…Nevertheless, we can now foresee any alterations to the plant or dangers to its development. Using RGB and multispectral imagery taken by a UAV, Mateen and Zhu [57] showed in their research that it is feasible to find weed patches in wheat. In contrast to other studies, which focused on a specific topic, Vergara et al [20] conducted a study which served as the foundation for our research, tackling the estimation of grain yield losses in yellow-rusted durum wheat using digital and conventional tools.…”

Section: Can Proximal Imaging or Uavs Be Used Accurately To Select Cu...mentioning

confidence: 99%

Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

Hamdane,

Segarra,

Buchaillot

et al. 2023

Drones

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The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R2 = 0.61–0.7 and R2 = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R2 = 0.25–0.35 and R2 = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties.

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WEED DETECTION IN WHEAT CROP USING UAV for PRECISION AGRICULTURE

Cited by 11 publications

References 18 publications

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

From Satellite to UAV-Based Remote Sensing: A Review on Precision Agriculture

Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

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