Classification of Weeds of Paddy Fields using Deep Learning
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Abstract
Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways - chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a problem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby limiting the negative impact of the chemical herbicides on the environment. This research work shows promising results with an accuracy around 90% and thus encourages the development of digital agriculture.
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