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

By Tanzeel U. Rehman, Md. Sultan Mahmud, Young K. Chang, Jian Jin, Jaemyung Shin. Computers and Electronics in Agriculture, 2019

343 citations recorded by the source corpus.

Abstract 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.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/19d4ae91f0d9794c27ec93632bdfa0d7a9744524

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. D. Saha
2. Annamalai Manickavasagan

In Ecology

1. Spyros Fountas

In Plant Science

1. D. Saha
2. Annamalai Manickavasagan

Citations in the corpus

• Agricultural Robotics for Field Operations (2020, background): “Better image detection systems that use more advanced algorithms are already under development [50].”
• Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review (2021, methodology): “Different machine learning techniques like Artificial Neural Network (ANN), Fuzzy logic, decision trees, Naïve Bayes, k-means clustering, support vector machines (SVM), random forest (RF), k-Nearest Neighbor (k-NN) and so on have been used extensively in agriculture related fields (Rehman et al., 20”
• IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry (2020, neutral): “Detailed discussions about ML methods and their agri-food specific applications can be found elsewhere [9, 13].”
• A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, methodology): “The analysis of data collected from sensors (Kale and Sonavane, 2018; Goap et al. 2018; Liakos et al. 2018) and drones (Fernández et al. 2018; Rehman et al. 2019; Rico-Fernández et al. 2019) deliver insights that enhances farm productivity and farmers profitability.”