Automated weed detection system in smart farming for developing sustainable agriculture

• Published in: International journal of environmental science and technology (Tehran) Vol. 19; no. 9; pp. 9083 - 9094
• Main Authors: Rani, S. V. Jansi, Kumar, P. Senthil, Priyadharsini, R., Srividya, S. Jahnavi, Harshana, S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
• Subjects: Aquatic Pollution; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Science and Engineering; Original Paper; Soil Science & Conservation; Waste Water Technology; Water Management; Water Pollution Control; Precision agriculture; Computer vision; Feature extraction; Classification; Machine learning
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-021-03606-6

In the Indian agricultural industry, weedicides are sprayed to the crops collectively without taking into consideration whether weeds are present. More intelligent methods should be adopted to guarantee that the soil and crops obtain exactly what they need for optimum health and productivity in smart agriculture. In smart farming industry, the use of robotic systems enabled with cameras for case-specific ministrations is on the rise. In this paper, the crop and weed have been efficiently differentiated by first applying the feature extraction methods followed by machine learning algorithms. The features of the weed and crop are extracted using speeded-up robust features and histogram of gradients . The logistic regression and support vector machine algorithms are used for classification of weed and crop. The method which used histogram of gradients for feature extraction and support vector machine for classification shows better results compared to other methods. This model is deployed on a field robot, weed detection system. The system helps in spraying weedicide only wherever it is required, thereby eliminating manual engagement with harmful chemicals and also reducing the number of toxic chemicals that enter through the food. This automated system ultimately helps in the sustainable smart farming for agricultural growth.