Modified crop health monitoring and pesticide spraying system using NDVI and Semantic Segmentation: An AGROCOPTER based approach

Tendolkar, Atharv; Choraria, Amit; Pai, M. M. Manohara; Girisha, S; D'souza, G. I.; Adithya, K. S.

doi:10.1109/icas49788.2021.9551116

Cited by 3 publications

(7 citation statements)

References 10 publications

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“…Crop Monitoring and Health Assessment. Drones equipped with high-resolution cameras and sensors capture detailed images of crops [ 105 ]. These images help to assess the health and vigor of plants by identifying areas with pest infestations, diseases, nutrient deficiencies, or water stress.…”

Section: Applications Of Copter Dronesmentioning

confidence: 99%

A Review on the State of the Art in Copter Drones and Flight Control Systems

Peksa,

Mamchur

2024

Sensors

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This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.

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Section: Applications Of Copter Dronesmentioning

confidence: 99%

A Review on the State of the Art in Copter Drones and Flight Control Systems

Peksa,

Mamchur

2024

Sensors

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“…Table 2 illustrates some of the possible vegetation indices that can be derived. Edge detectors can also be useful and are commonly used, such as Gaussian, Laplacian, and Canny filters [52]; Gabor filters, gray-level co-occurrence matrix (GLCM) [53], and geometric and statistical features [54] are among other useful features used in precision farming.…”

Section: Image Features Used In Uav Datamentioning

confidence: 99%

“…Support vector machines (SVM) were used for classifying vegetation by health status [52], classifying trees by type [61], identifying and classifying weeds to generate weed maps [62], and lastly, segmenting crop rows [53].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…Tendolkar et al [52] proposed the use of an Agrocopter, a multipurpose farming drone, to assess and evaluate plant health status and to take corrective actions. The system assessed plant health on the basis of the NVDI index, texture, and color features of the individual pixels.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…Some similarities can be observed. Many (e.g., [52,61,62]) used the NDVI scale to perform pixel-wise classification, while others (e.g., [53,62]) used the ExG index for classification. The authors of [53,61] used a radial basis function kernel to find the optimal hyperplane for separating the dataset classes.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

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Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

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Semantic Segmentation of Algal Blooms on the Ocean Surface Using Sentinel 3 Chl_nn Band Imagery

BHANDAGE,

PAI M. M.

2024

Appl. Comput. Sci.

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Satellite imagery plays an important role in detecting algal blooms because of its ability to cover larger geographical regions. Excess growth of Sea surface algae, characterized by the presence of Chlorophyll-a (Chl-a), is considered to be harmful. The detection of algal growth at an earlier stage may prevent hazardous effects on the aquatic environment. Semantic segmentation of algal blooms is helpful in the quantization of algal blooms. A rule-based semantic segmentation approach for the segregation of sea surface algal blooms is proposed. Bloom concentrations are classified into three different concentrations, namely, low, medium, and high. The chl_nn band in the Sentinel-3 satellite images is used for experimentation. The chl_nn band has exclusive details of the presence of chlorophyll concentrations. A dataset is proposed for the semantic segmentation of algal blooms. The devised rule-based semantic segmentation approach has produced an average accuracy of 98%. A set of 100 images is randomly selected for testing. The tests are repeated on 5 different image sets. The results are validated by the pixel comparison method. The proposed work is compared with other relevant works. The Arabian Sea near the coastal districts of Udupi and Mangaluru has been considered as the area of study. The methodology can be adapted to monitor the life cycle of blooms and their hazardous effects on aquatic life.

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Modified crop health monitoring and pesticide spraying system using NDVI and Semantic Segmentation: An AGROCOPTER based approach

Cited by 3 publications

References 10 publications

A Review on the State of the Art in Copter Drones and Flight Control Systems

A Review on the State of the Art in Copter Drones and Flight Control Systems

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Semantic Segmentation of Algal Blooms on the Ocean Surface Using Sentinel 3 Chl_nn Band Imagery

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