Abstract
On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400–700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.
Highlights
Varietal impurity and misidentification may be introduced in any step of the seed production chain, expanding from cultivation of the mother plants to processing
Varietal impurity may be the result of intentional adulteration via including seeds of another variety of reduced cost
Four commercially prominent chickpea varieties (Adel, Arman, Azad, and Saral) were evaluated. Both a mobile phone camera (LG V20, 16-megapixel resolution; LG Electronics Inc., Seoul, Korea) and a digital camera (PowerShot SX260 HS, 12.1-megapixel resolution and 6.2 × 4.6 mm sensor; Canon, Kyoto, Japan) were employed. The latter has a small sensor size, which is comparable with the cameras of mobile phones
Summary
Seeds represents the first crucial input. Employing seeds of the appropriate variety is a prerequisite to reach the optimum yield potential and secure a uniform product. Compromised seed varietal purity adversely affects cultivation practices, and eventually limits plant growth and productivity. On this basis, there is a growing pressure on the seed producers, processors, and distributors to assert seed purity. Varietal impurity and misidentification may be introduced in any step of the seed production chain, expanding from cultivation of the mother plants (pre-basic, basic, and certified material) to processing. Varietal impurity may be the result of intentional adulteration via including seeds of another variety of reduced cost. In this perspective, methods of variety identification and discrimination are highly needed
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