Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 50

Climate change posing serious challenges on fresh water and good soil and are becoming serious limitations for agriculture around the world. Average raise in temperatures was causing more extreme heat throughout the year. Rainfall patterns are also shifted more intense storms of short spells and longer dry periods. Severe droughts tolled heavily on crops, and livestock. On the other hand, increased floods destroy crops and livestock, accelerate erosionof soil, pollute fresh water, and damage roads and bridges. Sea level rise is also the intensity of floods on farms and sea water intrusion in coastal regions. New pests are boosting up and damaging crops [1].

It is also important to know that climate change risks are not constant and not distributed equally neither in space nor in time. In turn, it requires regular crop monitoring and management of resources to get maximum yields. The monitoring of crops at regional level includes crop type mapping, cropping pattern recognition, crop condition estimation, crop yield estimation, estimation of evapotranspiration, irrigation scheduling, monitoring of water resources, uncertainty analysis, Identification of pest attack, soil mapping, and so on [2]. Although agriculture is a complex interlinked phenomenon, clear-cut success has been achieved with the technological interventions in decision-making processes and in shaping adaptation strategies with the changing scenarios. Technological innovations like mechanization, artificial intelligence and robotics, UAVs, sensors, Internet of Things (IoT), remote sensing, machine learning, deep learning, and their combinations in agriculture have the ability to transform food chain of the crops. The integration of local agricultural knowledge with remote sensing depends on the understanding of complex phenomena in agriculture [3].

Crop yield estimation at regional level plays crucial role in planning for food security of the population. This is of greater important task for some wide applications, including management of land and water management, crop planning, water use efficiency, crop losses, economy calculation, and so on. Traditional ground observation-based methods of yield estimation, such as visual examination and sampling survey, require continuous monitoring, and regular recording of crop parameters [4–6]. Spectral information from remote sensing images gives very accurate crop attributes that can be used for crop mapping and estimation. Further integration of machine learning algorithm with remote sensing provides explicit estimation of yield [7]. The present study focused on ability of machine learning algorithm in integration with remote sensing in crop yield prediction of paddy and sugarcane crops at regional level.