John Nowatzki

Detection of soybean aphids in a greenhouse using an image processing technique

Soybean aphids affects crop growth and causes significant yield losses in soybean production.Manual scouting and counting of soybean aphids is laborious and time consuming.Algorithm developed based on image processing techniques is useful to detect and count aphids.Different image resolutions and illumination conditions affect accuracy of the algorithm. Soybean aphid (Aphis glycines) is one of the most important insect pests of soybeans in North America. Insecticide application is performed if the aphids count exceeds the economic threshold of 250 per plant. Precise estimates of aphid densities are needed for field conditions to maximize insecticide application efficiency. The current method of identifying and counting aphids on a plant is a labor-intensive and time consuming process. The objective of this study was to use image processing technique to detect and count different sized soybean aphids on a soybean leaf. The trials were conducted with soybean plants grown in a greenhouse. Three sets of data were collected on different dates using replicate plants from 4 soybean varieties infested with a range of aphid densities. Images of infested soybean trifoliate leaves were captured with different cameras under 2 different illumination conditions with different cameras used across the different data sets. The images captured were processed in MATLAB R2014a software using the Image Processing Toolbox to identify and count aphids. In order to evaluate the accuracy of the algorithm, the aphids counted with the sensing system were compared to a count generated manually by a trained expert. The algorithm counting with SONY camera images correlated (r2=0.96) very well with manual counts. The misclassification percentage was low for most cameras with different resolutions under high illumination conditions. The results also showed that images captured with an inexpensive regular digital camera gave satisfactory results under high illumination conditions.

Identification and Counting of Soybean Aphids from Digital Images using Particle Separation and Shape Classification

Abstract. Aphids population on soybean plants, usually assessed by manual counting, is essential to make pesticide application decisions. Pesticide is applied if the aphid counts exceed the economic threshold of 250 per plant. Manual counting is time-consuming, laborious, and causes visual fatigue. The objective of this study was to develop a method based on computer vision technique to count aphids on soybean leaves. The aphids infested soybean trifoliate were clipped from the greenhouse experiment at three infestation rates (low, medium, and high). Images were captured in the laboratory with three cameras (DSLR, consumer-grade digital camera, and smartphone camera) at two illumination conditions (sunny, and cloudy). The images were processed using a two-stage approach of segmentation followed by classification. In the first stage, image thresholding was performed with marker-controlled watershed segmentation for particle separation to identify the different objects in the image. In the second stage, the identified objects (aphids, exoskeleton, and leaf spots) were classified and counted using shape analysis. The proposed method not only identifies individual aphids, but also has the capability of identifying/resolving touching or overlapped aphids. This approach enables rapid automatic counting ( 2 =0.847).

Air Seeders for Conservation Tillage Crop Production

Air seeders are agricultural field crop planters that use air pressure to distribute seeds from a central seed tank to individual soil openers. The basic type of openers affects seed and fertilizer placement in the soil, seedling development and crop yields. The two basic opener designs used on air seeders are disc and hoe openers. Crop producers choose opener types based on the amount and type of crop residue, the crop being planted, fertilizer placement, and soil type and conditions. Disc openers function better in standing residue rather than in conditions where the residue is cutoff and laying on the soil surface; hoe openers generally function better in these conditions. Row cleaners, usually spoked wheels mounted in front of disc openers, can be used to facilitate planting fields with high residue.