Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1

Applications of Emerging Smart Technologies in Farming Systems: A Review © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 61 ( )G Year 2023 mining is utilized to explore different soil types. ( Bagal Yash V et al. 2020 ) Analysts of soil data propose the crop to be planted and harvested based on the soil's fertility to provide the optimum yield. Data mining can also be used to study cross-cultivation ( Bagal Yash V et al. 2020 ). Different crops can be grown simultaneously, bringing in more revenue than single-crop cultivation and utilizing resources to the best possible extent without affecting soil fertility. The scope of data mining is enormous, and its scope can be seen in the soil analysis as follows ( Bagal Yash V et al. 2020 ). 1. Crops can be grown by sensing and detecting soil capabilities. 2. Previously unknown soil patterns can be discovered. 3. Soil traits and behavior can be predicted based on climate conditions and ingredients. 4. d) Soil fertility testing can be done using statistical methods. iii. Optimizing the use of Pesticides Agricultural researchers revealed in a recent study that pesticides are overused, which is highly hazardous for the environment. Additionally, overuse of pesticides can result in pest immunity, which makes them less vulnerable to control and ultimately more harmful to crops. Clustering is one of the data mining methods that can cluster the features by providing interesting patterns of farmer practices and thus provide meaningful information highlighting the negative effect of excessive pesticide use. ( Bagal Yash V et al. 2020 ). The system employs an image processing mechanism based on aspect ratios, shapes, and surface area. Later, images of the cultivation area are processed to detect weed patches using specific algorithms. Color density in the images is used to represent the density of crop growth in a specific area, whereas a different color represents irregular crop growth. ( Bagal Yash V et al. 2020 ). iv. D. Prediction of wine Fermentation This Prediction can be made using the k-means Data Mining technique ( Han and Kamber, 2006 ). This Prediction can warn the chemist to fix any stuck or slow fermentation processes and ensure a good fermentation process. v. Weather Forecasts A k-nearest neighbor approach can be used to improve weather forecasting, where it is assumed that the climate during a specific year is similar to the one recorded in the past. The same data mining technique can also be used to estimate soil water parameters. Before marketing, apples, and other fruits are thoroughly examined in agriculture. Humans can inspect apples on conveyor belts, and bad apples (those with defects) can be removed. The data mining tasks can perform the same task efficiently. The apple water core is examined using X-ray images in this task. It is based on an artificial neural network that learns how to classify X-ray images from a training set. (Winter School Notes of ICAR- IASRI2011). III. I ssues & C hallenges Smart technologies are a boon for the farming community in many ways, but they pose challenges. The main challenges are privacy, reliability, data confidentiality, and security. The Weather Company, an IBM business, held a first-of-its-kind event titled The AgriTech Challenge 2018 in Mumbai on 13 June 2018 to find solutions to transform the lives of over three million Indian farmers. The event was held in association with the Agripreneurs Group, SMART AGRIPOST and Graype.in and discussed the top challenges faced by the whole ecosystem in adopting smart technologies. a) Cost of Technology Smart technologies such as machine learning, robotics, IoT, big data analytics, bioinformatics etc., necessitate expensive equipment. However, while sensors are the least expensive, outfitting at farmers' fields would cost more. Moreover, automated machinery is far more expensive than manually operated machinery since it covers the cost of agricultural management solutions software and cloud access to record data. However, Farmers are eager to invest in these techniques to enhance their earnings, but they might need help to raise the initial investment to set up a smart farm. b) Poor Bandwidth and Internet C onnectivity Adopting digital technolo gies in rural farmer's fi elds will be im proved by providing better network connectivity and adequate bandwidth speeds. Smart farming agriculture technologies, including satellite mapping systems, soil sensors, and many monitoring too ls, rely on c loud services/cloud-based computing for dat a storage a nd retrieval. These services might be compromised by inadequate bandwidth. Furthermore, farms with large, dense trees and hilly terrain must receive GPS signals seamlessly, making it much more challenging to use smart farming techniques there. Implementing smart technologies in remote rural areas may be difficult due to limited electricity and network coverage. c) Lack of Technical Know-how in Farming and Allied Workforce In general, farmers are typically ignorant and unskilled, and many of them would not prefer to learn about new technologies. On the other hand, policymakers have yet to make enough efforts to create online /offline capacity-building programs for farmers in locally relevant content. Lack of technical expertise in handling smart farming setup and bad implementation, such as installing a sensor in the wrong place or forgetting to switch off the irrigation tank, might harm