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Molin, J
Kantipudi, K
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Authors
Maldaner, L
Canata, T
Molin, J
Passalaqua, B
Quirós, J.J
Passalaqua, B.P
Molin, J
Salvi, J
Aguilera, A.P
Maldaner, L
Molin, J
Tavares, T
Mendez, L
Corrêdo, L
Duarte, C
Kantipudi, K
Lai, C
Min, C
Chiang, R.C
Ferraz, M.N
Trevisan, R.G
Eitelwein, M.T
Molin, J
Karp, F.H
Topics
Engineering Technologies and Advances
Geospatial Data
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Poster
Oral
Year
2016
2018
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Authors

Filter results5 paper(s) found.

1. Static and Kinematic Tests for Determining Spreaders Effective Width

Spinner box spreaders are intensively used in Brazil for variable rate applications of lime in agriculture. The control of that operation is a challenging issue because of the complexity involved on the interactions between product and machine. Quantification of transverse distribution of solids thrown from the spinner box spreaders involves dynamic conditions tests where the material deposited on trays is evaluated along the pass of the machinery. There is a need of alternative testing methods... L. Maldaner, T. Canata, J. Molin, B. Passalaqua, J.J. Quirós

2. Misalignment Between Sugar Cane Transshipment Trailers and Tractor

Sugarcane production system is dependent on a continuous cutting and regrowth of cane plants from their roots, on which traffic should be avoided to ensure the physiological integrity of regrowth and productivity.  This need for accuracy in sugarcane machine traffic boosted the adoption of automated steering systems, especially on harvesters. Tractors with the transshipment trailers, which continually accompany the harvesters in the field, yet do not adopt it or use technology with lower... B.P. Passalaqua, J. Molin, J. Salvi, A.P. Aguilera

3. Identifying and Filtering Out Outliers in Spatial Datasets

Outliers present in the dataset is harmful to the information quality contained in the map and may lead to wrong interpretations, even if the number of outliers to the total data collected is small. Thus, before any analysis, it is extremely important to remove these errors. This work proposes a sequential process model capable of identifying outlier data when compared their neighbors using statistical parameters. First, limits are determined based on the median range of the values of all the... L. Maldaner, J. Molin, T. Tavares, L. Mendez, L. Corrêdo, C. Duarte

4. 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

5. Soybean Plant Phenotyping Using Low-Cost Sensors

Plant phenotyping techniques are important to present the performance of a crop and it interaction with the environment. The phenotype information is important for plant breeders to analyze and understand the plant responses from the ambient conditions and the inputs offered for it. However, for conclusive analysis it is necessary a large number of individuals. Thus, phenotyping is the bottleneck of plant breeding, a consequence of the labor intensive and costly nature of the classical phenotyping.... M.N. Ferraz, R.G. Trevisan, M.T. Eitelwein, J. Molin, F.H. Karp