Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Abstract

Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.

Machine Learning in Agriculture: A Comprehensive Updated Review

“…Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ].…”

“…In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [ 15 , 21 , 22 , 23 ]. Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ].…”

“…Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ].…”

Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

“…In the industrial context, optimal wavelength selection can reduce the number of wavelengths but can maintain their accuracy, which is preferable for industrial settings equipped with low-cost spectrometers [23]. Several spectra variables are selected from the full wavelength region and can be applied to develop a simple spectrometer system to detect the characteristics of the material of interest [24,25]. The main contribution of the application of NIR hyperspectral imaging could be in enabling the application of a machine learning algorithm to find the optimal wavelength selection and improve its ability based on spectral pro-processing.…”

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

“…However, the problem with chemical weeding is the full coverage field spraying of herbicides without distinguishing between crops and weeds, which causes much herbicide waste, increases pollution, and increases soil dependence on chemical agents [ 1 ]. With the maturity and improvement of image processing, machine vision technology, and the development needs of precision agriculture, countries have begun to study the use of computer vision technology to achieve the precise use of herbicides [ 2 , 3 , 4 ]. In addition, precise fertilization has also become a trend.…”