“…It is widely used in agriculture, oceanography, geology, atmosphere, and environmental sensing [ 19 ]. In agriculture, hyperspectral techniques can be used for pest and disease monitoring [ 20 , 21 , 22 , 23 , 24 , 25 ], biomass estimation [ 26 ], and biological stress monitoring [ 27 ]. In the field of pest and disease monitoring, it has been applied to many plants, including tomatoes, grapes, and wheat [ 20 , 21 , 22 ].…”
Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.
“…It is widely used in agriculture, oceanography, geology, atmosphere, and environmental sensing [ 19 ]. In agriculture, hyperspectral techniques can be used for pest and disease monitoring [ 20 , 21 , 22 , 23 , 24 , 25 ], biomass estimation [ 26 ], and biological stress monitoring [ 27 ]. In the field of pest and disease monitoring, it has been applied to many plants, including tomatoes, grapes, and wheat [ 20 , 21 , 22 ].…”
Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.
“…To determine a whitefly infestation, Nigam et al . (2016) determined the relation of the infestation with and biotic stress with remote sensors. They used a spectroradiometer with different sampling intervals across the spectral region of 350–2500 nm at 1 nm.…”
Section: Resultsmentioning
confidence: 99%
“…The degree of infestation is rapid, for example, the whitefly can lay 130 eggs (Boissard et al ., 2008), which is why early detection of this pest is important. Image recognition was mainly used, but the analysis of environmental conditions was also important to discriminate biotic stress (Nigam et al ., 2016) that can encourage the appearance of insect pests; for instance, this is the case of the whitefly which develops rapidly in warm weather. Fig.…”
Section: Discussionmentioning
confidence: 99%
“…In the case of in-situ sensors, environment-temperature sensors, humidity sensors, soil-moisture sensors, leaf-wetness sensors and water sensors were used (Nigam et al, 2016;Sarangdhar and Pawar, 2017). In the selected studies, it was demonstrated that with low-cost sensors, it was possible to take enough information to make predictions of insect pests or diseases.…”
Integrated pest management (IPM) seeks to minimize the environmental impact of pesticide application, and reduce risks to human and animal health. IPM is based on two important aspects – prevention and monitoring of diseases and insect pests – which today are being assisted by sensing and artificial-intelligence (AI) techniques. In this paper, we surveyed the detection and diagnosis, with AI, of diseases and insect pests, in cotton, which have been published between 2014 and 2021. This research is a systematic literature review. The results show that AI techniques were employed – mainly – in the context of (i) classification, (ii) image segmentation and (iii) feature extraction. The most used algorithms, in classification, were support vector machines, fuzzy inference, back-propagation neural-networks and recently, convolutional neural networks; in image segmentation, k-means was the most used; and, in feature extraction, histogram of oriented gradients, partial least-square regression, discrete wavelet transform and enhanced particle-swarm optimization were equally used. The most used sensing techniques were cameras, and field sensors such as temperature and humidity sensors. The most investigated insect pest was the whitefly, and the disease was root rot. Finally, this paper presents future works related to the use of AI and sensing techniques, to manage diseases and insect pests, in cotton; for instance, implement diagnostic, predictive and prescriptive models to know when and where the diseases and insect pests will attack and make strategies to control them.
“…For example, alterations to NDVI and other spectral indices show strong relationships with the fraction of absorbed photosynthetically active radiation (Viña and Gitelson 2005), which is a critical index for inclusion in production efficiency models (Roujean and Breon 1995). Remote sensing techniques are also showing considerable value in identifying crop pests and diseases (e.g., Mahlein 2016), including powdery mildew (Yuan et al 2016) and white fly (Nigam et al 2016), among others. Solar-induced fluorescence (SIF; Yang et al 2015) indicates photosynthetic activity with space-based monitoring capabilities (Guan et al 2015), and gross primary productivity (GPP; Running et al 2000) can indicate biomass and carbon allotment for use in crop modeling.…”
Section: B Use Of Satellite Data For Agriculturementioning
Applied meteorology is an important and rapidly growing field. This chapter concludes the three-chapter series of this monograph describing how meteorological information can be used to serve society’s needs while at the same time advancing our understanding of the basics of the science. This chapter continues along the lines of Part II of this series by discussing ways that meteorological and climate information can help to improve the output of the agriculture and food-security sector. It also discusses how agriculture alters climate and its long-term implications. It finally pulls together several of the applications discussed by treating the food–energy–water nexus. The remaining topics of this chapter are those that are advancing rapidly with more opportunities for observation and needs for prediction. The study of space weather is advancing our understanding of how the barrage of particles from other planetary bodies in the solar system impacts Earth’s atmosphere. Our ability to predict wildland fires by coupling atmospheric and fire-behavior models is beginning to impact decision-support systems for firefighters. Last, we examine how artificial intelligence is changing the way we predict, emulate, and optimize our meteorological variables and its potential to amplify our capabilities. Many of these advances are directly due to the rapid increase in observational data and computer power. The applications reviewed in this series of chapters are not comprehensive, but they will whet the reader’s appetite for learning more about how meteorology can make a concrete impact on the world’s population by enhancing access to resources, preserving the environment, and feeding back into a better understanding how the pieces of the environmental system interact.
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