Abstract
This paper presents a novel vision based approach for detecting rows of crop in paddy field. The precise detection of crop row enables a farm-tractor to autonomously navigate the field for successful inter-row weeding. While prior works on crop row detection rely primarily on various image based features, a deep neural network based approach for learning semantic graphics to directly extract the crop rows from an input image is used in this work. A deep convolutional encoder decoder network is trained to detect the crop lines using semantic graphics. The detected crop lines are then used to derive control signal for steering the tractor autonomously in the field. The results demonstrate that the proposed method is able to detect the rows of paddy accurately and enable the tractor to navigate autonomously along the crop rows even with a simple proportional only controller.
Highlights
The increase in global population has led to an increase in the demand of agricultural food products to feed them
In this paper we propose to use a deep neural networks (DNNs) based system for detecting the rows of crop in row-transplanted paddy field using semantic graphics, and demonstrate that the detected crop rows can be used to guide a tractor to navigate autonomously in the field
In this current work we extend the concept of learning semantic graphics to extract crop rows and use the rows to guide a farm tractor autonomously in a paddy field
Summary
The increase in global population has led to an increase in the demand of agricultural food products to feed them. With limited availability of resources, ramping up food production to meet the ever-increasing demand is a challenging task. Researchers, engineers and farmers have come up with several ingenious solutions like better farming techniques, precision farming, farm automation etc. Farming and most of the associated tasks are highly labor-intensive. Though human population has been increasing there has been a constant decline in the share of labor force working in agriculture [1] due to the laborintensive and repetitive nature of the work. While much of the agricultural tasks have already been mechanized resulting in reduced human labor, researchers have been working towards reducing the reliance on human labor with automation and keeping it to minimal
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