Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Visual perception refers to the process of organizing, identifying, and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this article, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this article also summarizes the current challenges of visual perception and predicts its future development trends.

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Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

This paper presents the concerns in agri-food supply chain. Further the research investigates the role of information and communication technology (ICT) in agri-food supply chain and determines the impact of supply chain management (SCM) practices on firm performance.,The theoretical framework was proposed for the study on the basis of existing literature. Data for the study was collected with the help of structured questionnaire from 121 executives and officers of the public food distribution agency. Partial least square (PLS)–structured equation modeling was employed to test the framework and hypotheses.,The results indicate that ICT and SCM practices (logistics integration and supplier relationships) have a significant relationship. Furthermore, SCM practices (information sharing, supplier relationship and logistics integration) have a significant and positive impact on performance of the organization.,Further research could be carried out to test the moderation effect of SCM practices between ICT and organizational performance (OP). Extending the research study to the companies operating in other sectors can enhance the external validity of the study and improve the accuracy of parameters examined.,This study can be of interest to the agri-food industry as well as other industry practitioners interested in improving the performance of the organization from the view of supply chain.,The outcomes of this study have important implications that translate into a series of recommendations for the management of public food distribution as well as other agri-food-based supply chains.

Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain

Interest in agricultural automation has increased considerably in recent decades due to benefits such as improving productivity or reducing the labor force. However, there are some current problems associated with unstructured environments make developing a robotic harvester a challenge. This article presents a dual-arm aubergine harvesting robot consisting of two robotic arms configured in an anthropomorphic manner to optimize the dual workspace. To detect and locate the aubergines automatically, we implemented an algorithm based on a support vector machine (SVM) classifier and designed a planning algorithm for scheduling efficient fruit harvesting that coordinates the two arms throughout the harvesting process. Finally, we propose a novel algorithm for dealing with occlusions using the capabilities of the dual-arm robot for coordinate work. Therefore, the main contribution of this study is the implementation and validation of a dual-arm harvesting robot with planning and control algorithms, which, depending on the locations of the fruits and the configuration of the arms, enables the following: (i) the simultaneous harvesting of two aubergines; (ii) the harvesting of a single aubergine with a single arm; or (iii) a collaborative behavior between the arms to solve occlusions. This cooperative operation mimics complex human harvesting motions such as using one arm to push leaves aside while the other arm picks the fruit. The performance of the proposed harvester is evaluated through laboratory tests that simulate the most common real-world scenarios. The results show that the robotic harvester has a success rate of 91.67% and an average cycle time of 26 s/fruit.

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Robotic Aubergine Harvesting Using Dual-Arm Manipulation

Computer vision technology in agricultural automation —A review

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

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Applications of UAS in Crop Biomass Monitoring: A Review

Modal complementary fusion network for RGB-T salient object detection

Grasping detection is one of the essential tasks for robots to achieve automation and intelligence. The existing grasp detection mainly relies on data-driven discriminative and generative strategies. Generative strategies have significant advantages over discriminative strategies in terms of efficiency. RGB and depth (RGB-D) data are widely used in grasping data sources due to the sufficient amount of information and low cost of acquisition. RGB-D fusion has shown advantages over only using RGB or depth. However, existing research has mainly focused on early fusion and late fusion, which is challenging to utilize information from both modalities fully. Improving the accuracy of grasping while leveraging the knowledge of both modalities and ensuring lightweight and real time is crucial. Therefore, this article proposes a pixel-wise RGB-D dense fusion method based on a generative strategy. The technique is doubly experimentally validated on public datasets and real robot platform. Accuracy rates of 98.9% and 94.0% are achieved on Cornell and Jacquard datasets, and the efficiency of only 15 ms is achieved for single-image processing. The average success rate of the AUBO i5 robotic platform with DH-AG-95 parallel gripper reached 94.0% for single-object scenes, 86.7% for three-object scenes, and 84% for five-object scenes. Our approach has outperformed existing state-of-the-art methods.

Lightweight Pixel-Wise Generative Robot Grasping Detection Based on RGB-D Dense Fusion

Deep neural networks have greatly improved the performance of rail surface defect segmentation when the test samples have the same distribution as the training samples. However, in practical inspection scenarios, the rail surface exhibits variations in appearance due to different service time and natural conditions. Conventional deep learning models show limited generalization in scenes with distribution differences. To address this problem, we propose a novel one-shot unsupervised domain adaptation framework. Specifically, we introduce a shape consistent style transfer module that performs pixel-level distribution alignment between the training and test images. Based on the one-shot test image, the training image is reconstructed to have the same appearance as the test image. Meanwhile, we employ a multi-task learning strategy to prevent content distortion of the reconstructed images. To improve the robustness of the model to distribution differences, we design an edge-aware defect segmentation model and train the model using the reconstructed training images. The experimental results show that our method effectively improves the robustness of the model to distribution differences and achieves satisfying results in the task of rail surface defect segmentation. 

Hongkun Tian

Papers

Computer vision technology in agricultural automation —A review

Applications of UAS in Crop Biomass Monitoring: A Review

Modal complementary fusion network for RGB-T salient object detection

Lightweight Pixel-Wise Generative Robot Grasping Detection Based on RGB-D Dense Fusion

Shape Consistent One-Shot Unsupervised Domain Adaptation for Rail Surface Defect Segmentation