Sensor data fusion and processing in smart agriculture: crop quality assessment, crop damage, smart planning

Agricultural lands are generally vast, remote, and sensitive to weather, making data collection difficult. Despite these limitations, additional data are being captured as technologies evolve and prices reduce. Proximal, aerial, satellite, and ancillary data are obtained to study the crop for various objectives of smart agriculture. Geographically unequal areas lack vital data to address their challenges. Even in regions with adequate infrastructure and resources, data being obtained for agricultural environments produce knowledge gaps. Many aspects must be considered and measured for a region's whole description or examination of the targeted problem. Data from a single type of sensor is not very effective for algorithms, and results are imprecise. However, fusing sensor data is one of the key approaches for crops' quality assessment, damage, and smart planning for their management. The data fusion explores complementarities and synergies of extracting trustworthy and relevant information from data. Exploring the correspondence and interactions between various types of data is the core concept behind data fusion, which aims to derive more trustworthy and applicable information regarding the topics being investigated. Despite some success, several obstacles still limit a more general adoption of this type of strategy. This is especially true for the highly complicated ecosystems that can be found in rural and agricultural settings. In this part of book, we provide a comprehensive review on data fusion to agricultural problems and study of plant disease using different sensor data for smart agriculture. Conclusively, the study contributes on emphasizing the data acquisition scales, data types, and their fusion for decision making in smart agriculture. Ghulam Mustafa1 and Qurban Ali contributed equally to this chapter.