Читать книгу Machine Learning Algorithms and Applications - Группа авторов - Страница 29

In the last decade, machine learning has gained the popularity that no sequential programming approach has reached in a century in various fields of engineering. Deep learning/convolution neural network (CNN) is a part of a machine learning approach that solves a given problem without explicitly providing the features to be considered to generate useful results. These advanced techniques are predominantly deployed in the engineering field. However, disciplines such as medical imaging [1], microbiology [2], and finance [3] are vastly adopting machine learning to achieve superior results.

Further, some areas of science still use the conventional approach of solving the problem, and sericulture is one among them. The sericulture industry involves the art and science of host plant cultivation as well as silkworm rearing to produce natural silk products. Silk is the queen of textiles and globally India is the second-largest producer of four different types of silk. Thus, sericulture serves as the base for economic, social, scientific, political, and intellectual advancements [4]. The fecundity (number of eggs laid by fertilized female silk moth), hatching percentage (silkworm birth rate), survival percentage (disease and environment tolerant), and silk productivity are a few economic traits (parameters) on which entire silk industry thrives. Manual counting of eggs is in vogue to quantify fecundity and hatching percentage parameters. Many automatic methods (image processing and new hardware design) have been attempted with lower accuracy [5]. A new approach of automatic counting and classifying eggs is described in this paper to quantify fecundity and hatching percentage accurately which provides required rearing information to harvest successful silk cocoon crops.

The chapter describes a few conventional approaches and their drawbacks and, further, introduce the CNN approach adopted in this paper and to explain the specifications of each model trained to surpass the results provided by other image processing techniques.