Multi-purposeful Application of Geospatial Data

Agriculture is the backbone of the Indian economy. Any changes in weather and climate in short term as well as long- term adversely affect the agricultural productivity and the production of food grain production. In order to minimise the adverse impact of weather and climate on crops, the use of agrometeorological information and agromet services has already been proved to be highly beneficial. Agrometeorological services rendered by India Meteorological Department (IMD), Ministry of Earth Sciences, are a step to contribute to weather information-based crop/livestock management strategies and operations dedicated to enhance crop production and food security. IMD is operating a project ‘Gramin Krishi Mausam Sewa’ (GKMS) with an objective to serve the farming community at different parts of the country. Different states of technologies including the application of geospatial technology are being used in India for further refinement of the Agromet Advisory Services. The application of geospatial technology in generating agrometeorological information and products is very necessary for preparing need-based advisories at a high-resolution scale for the farmers in the country. In this chapter, elaborate discussion has been made on how the Geographical Information System (GIS) is being used for generating information and products using ground observations as well as satellite observations

Radar, Insect Population Ecology, and Pest Management

Discussions included: (1) the potential role of radar in insect ecology studies and pest management; (2) the potential role of radar in correlating atmospheric phenomena with insect movement; (3) the present and future radar systems; (4) program objectives required to adapt radar to insect ecology studies and pest management; and (5) the specific action items to achieve the objectives

Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

Impact of Climate Change on Plant Diseases and IPM Strategies

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases. Climate change influences the occurrence, prevalence, and severity of plant diseases. Projected atmospheric and climate change will thus affect the interaction between crops and pathogens in multiple ways. This will also affect disease management with regard to timing, preference, and efficacy of chemical, physical, and biological measures of control and their utilization within integrated pest management (IPM) strategies. Prediction of future requirements in disease management is of great interest for agro-industries, extension services, and practical farmers. A comprehensive analysis of potential climate change effects on disease control is difficult because current knowledge is limited and fragmented and due to the complexity of future risks for plant disease management, particularly if new crops are introduced in an area. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs. All these efforts and integrations will produce effective crop protection strategies using novel technologies as appropriate tools to adapt to altered climatic conditions

Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)

After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS).Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)publishedVersio

Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.info:eu-repo/semantics/publishedVersio

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted

Modelling and monitoring for strategic yield gap diagnosis in the South African sugar belt

AbstractThis paper revisits the diagnostic use of industry-wide sugarcane (Saccharum sp. hybrid) modelling and monitoring in South Africa for gaining a better understanding of production trends and the strategies required to address temporal and spatial yield variation.Such reviews have been conducted annually since 2008, by comparing the ratio of actual to simulated (potential) average sugarcane yields for 14 sugar mills with that of preceding seasons (since 1980). Actual yields are determined from total amount of cane crushed at the mill and the estimated area harvested as determined from mill records and grower surveys. Potential yields are determined by using the Canesim model with daily weather data for 48 homogenous agro-climatic zones. Widening yield gaps in some key producing regions and significant differences between regions indicated the need to investigate the impact of non-climatic factors such as pests, diseases, and sub-optimal agronomic management, even though this analysis is still qualitative and incomplete, and not fully objective. Factors that were highlighted as likely causes of suboptimal production were damaging effect of a new pest (sugarcane thrips), inadequate nutrition and inadequate replanting, apparently linked to unfavourable socio-economic conditions; even more so for small-scale growers than for large-scale growers. In addition to providing a service that is valued by the industry, the annual reviews have contributed to strengthening co-operation between researchers of distinct disciplines as well as between researchers and canegrowers, and to help identify priorities for further research. The quality of the analysis could be further improved by more accurate and timely estimates of the area harvested, improved resolution of yield data and extended surveys of pests, diseases and other yield limiting or reducing factors