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 2 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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%

See 2 more Smart Citations

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

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 2 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

Contact Info

Product

Resources

About