Corn leaf detection using Region based convolutional neural network

TL;DR

This paper introduces a region-based convolutional neural network approach, combining ResNet with Faster R-CNN, to improve accuracy and speed in corn leaf detection amidst weeds, outperforming existing models.

Contribution

The paper proposes a novel combination of ResNet and Faster R-CNN for corn leaf detection, addressing weed occlusion challenges with improved accuracy and efficiency.

Findings

Outperforms existing CNN models in corn leaf detection accuracy.

Effective in heavy weed occlusion scenarios.

Achieves faster detection times.

Abstract

The field of machine learning has become an increasingly budding area of research as more efficient methods are needed in the quest to handle more complex image detection challenges. To solve the problems of agriculture is more and more important because food is the fundamental of life. However, the detection accuracy in recent corn field systems are still far away from the demands in practice due to a number of different weeds. This paper presents a model to handle the problem of corn leaf detection in given digital images collected from farm field. Based on results of experiments conducted with several state-of-the-art models adopted by CNN, a region-based method has been proposed as a faster and more accurate method of corn leaf detection. Being motivated with such unique attributes of ResNet, we combine it with region based network (such as faster rcnn), which is able to automatically detect corn leaf in heavy weeds occlusion. The method is evaluated on the dataset from farm and we make an annotation ourselves. Our proposed method achieves significantly outperform in corn detection system.