Proceedings :: International Society of Precision Agriculture

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Chiang, R.C

Conley, S

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Authors

Herrmann, I

Vosberg, S

Ravindran, P

Singh, A

Townsend, P

Conley, S

Kantipudi, K

Lai, C

Min, C

Chiang, R.C

Topics

Precision Agriculture and Global Food Security

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2018

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Filter results2 paper(s) found.
1. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in Soybean Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley Download paper
2. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang Download paper

Filter results2 paper(s) found.

1. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in Soybean

Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley

Download paper

2. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows

One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang

Download paper