Abstract
Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever way to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and analyzing big data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications.
TopIntroduction
Modern agriculture relies heavily on smart agriculture to estimate crop yields and identify decision-making (Sehgal Foundation, 2023). Currently, crop productivity may fail due to insufficient farmers, agriculture workforce, weather conditions, etc. Introducing smart agriculture (Eos 2022) can improve crop productivity and monitor crop and agricultural pests. It can make agriculture more efficient and effective. UNCTAD (2017) reported that smart agriculture could reduce production costs while increasing agricultural yield and encouraging the effective use of agriculture resources, including human labor, energy, fertilizer, and water utilization.
According to Kathleen Walch (2020), farmers are better equipped to track all processes and apply specific actions identified by machines through superhuman accuracy with the most recent breakthroughs in connectivity, automation, and technologies. Artificial Intelligence (AI) can make judgments for farmers and predictions using sensor data. Farmers and data scientists are still evolving approaches to maximize the workforce needed in farming (Wang et al., 2021). Hence, smart farming has evolved into a learning system and has become even more inventive as vital information resources improve daily.
Artificial Intelligence in Smart Agriculture
Farmers can employ AI algorithms to estimate how much light their crops' foliage gets. AI approaches (Intellias 2022) can improve crop productivity in smart agriculture. AI systems using visual abilities can monitor and assess daily plant variations to calculate the growth rate. (Subeesh & Mehta, 2021). Most smartly, agricultural productivity can be increased using AI techniques such as machine learning (ML) and deep learning (DL).
ML and DL Applications can find and fix problems with agricultural growth (Hugo Storm et al., 2020). ML can improve the iterative process by learning from patterns and associations between them when making decisions. With higher prediction outcomes, the ML algorithm tries to produce accurate output. By classifying diverse crop yields, ML can enhance eyesight and improve the quality of images (Cravero et al., 2022).
Why Deep Learning in Smart Agriculture?
Crop management can be accelerated by smart agriculture. Since there are several factors, including climate and genetics, it is difficult to predict the yield of crops. Farmers may use intelligent technologies such as deep learning to accurately anticipate crop yields once they know how these elements affect crop yields. DL is an ML technique using artificial neural networks (ANNs) principles (Sarker, 2022). According to Kuradusenge et al. (2023), using DL methods to foretell agricultural diseases is practical and affordable. The DL approaches enhance agricultural research's capacity to discern the picture classification of agriculture. DL can be applied to various smart agriculture (Zhang et al., 2022) areas such as automating weed detection, classification of crops, collecting and extracting information about cultivated land, estimating crop yield (for example, the number of tomatoes in the plant), identifying and classifying leaves of different plant species, identifying plant diseases out of healthy leaves, identify a variety of spatial patterns, predict the growth of animals, predict the soil moisture content over an irrigated field, and predict weather conditions based on historical data.
Key Terms in this Chapter
Climate Change: Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer.
Precision Agriculture (PA): It is a farming management strategy based on observing, measuring, and responding to temporal and spatial variability to improve agricultural production sustainability.
Agricultural Innovation System: A system of individuals, organizations, and enterprises focused on bringing new products, processes, and economic use to achieve food and nutrition security, economic development, and sustainable natural resource management.
Ecosystem: The interactive system formed from all living organisms and their abiotic environment within a given area. It covers and comprises the entire globe, biomes at the continental scale, or minor, well-circumscribed systems such as a small pond.
Climate-Smart Agriculture (CSA): Helps guide actions to transform agri-food systems towards green and climate-resilient practices.
Erosion: The process of removal and transport of soil and rock by weathering, mass wasting, and the action of streams, glaciers, waves, winds, and underground water.
Smart Farming: It refers to managing farms using modern ICT to increase the quantity and quality of products while optimizing the human labor required.
Sustainability: Meeting the needs of the present without compromising the ability of future generations to meet their needs.
Disaster: A serious disruption of the functioning of a community or a society involving widespread human, material, economic, or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its resources.
Biodiversity: The total diversity of all organisms and ecosystems at various spatial scales (from genes to entire biomass).
Conservation Agriculture (CA): Conservation Agriculture is an approach to managing agroecosystems for improved and sustained productivity, increased profits, and food security while preserving and enhancing the resource base and the environment.
Smart Agriculture: It refers to using technologies like IoT, sensors, location systems, robots, and AI on the farm to increase the quality and quantity of the crops while optimizing human labor.