Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

• Published in: Computers and electronics in agriculture Vol. 156; pp. 585 - 605
• Main Authors: Rehman, Tanzeel U., Mahmud, Md. Sultan, Chang, Young K., Jin, Jian, Shin, Jaemyung
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2019; Elsevier BV
• Subjects: Agricultural equipment; Agricultural land; Agricultural practices; Agriculture; Algorithms; Artificial intelligence; Crop production; Discriminant analysis; Fuzzy clustering; Gaussian mixture model; K-means clustering; k-Nearest Neighbour; Machine learning; Machine vision; Naïve Bayes; Nondestructive testing; Population (statistical); Statistical machine learning; Support vector machines; Vision systems; Support vector machines; k-Nearest Neighbour; Naïve Bayes; Discriminant analysis; Fuzzy clustering; Machine vision; Statistical machine learning; K-means clustering; Gaussian mixture model
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.12.006

•Overview of agricultural machine vision system using statistical ML algorithms.•Supervised statistical ML algorithms include naïve Bayes, DA, kNN and SVMs.•Unsupervised ones include K-means clustering, Fuzzy clustering and GMM.•Highlight the limitations of different statistical ML algorithms in agriculture.•Suggest effective statistical ML algorithms in each specific area in agriculture. With being rapid increasing population in worldwide, the need for satisfactory level of crop production with decreased amount of agricultural lands. Machine vision would ensure the increase of crop production by using an automated, non-destructive and cost-effective technique. In last few years, remarkable results have been achieved in different sectors of agriculture. These achievements are integrated with machine learning techniques on machine vision approach that cope with colour, shape, texture and spectral analysis from the image of objects. Despite having many applications of different machine learning techniques, this review only described the statistical machine learning technologies with machine vision systems in agriculture due to broad area of machine learning applications. Two types of statistical machine learning techniques such as supervised and unsupervised learning have been utilized for agriculture. This paper comprehensively surveyed current application of statistical machine learning techniques in machine vision systems, analyses each technique potential for specific application and represents an overview of instructive examples in different agricultural areas. Suggestions of specific statistical machine learning technique for specific purpose and limitations of each technique are also given. Future trends of statistical machine learning technology applications are discussed.