International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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Application of Machine Learning to Enhance Crop Production Purushottam Varshney1, Paras Chandra2, Rohan Kumar Sinha3, Ms. Sakshi Malhotra4 1 B. Tech student, Information Technology, Galgotias College of Engineering & Technology, Uttar Pradesh, India
2 B. Tech student, Information Technology, Galgotias College of Engineering & Technology, Uttar Pradesh, India 3
B. Tech student, Information Technology, Galgotias College of Engineering & Technology, Uttar Pradesh, India
4 B. Tech faculty, Information Technology, Galgotias College of Engineering & Technology, Uttar Pradesh, India
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Abstract - Agriculture is the main source of earning for
technically skilled, so they require a smart solution that can assist them in increasing the efficiency and profitability of cultivation. And those solutions can be produced by smart agriculture.
various farmers. Their earning largely depends on the crop yield and the amount paid for their crops. In India, the crop yield is not up to the expectation. This happens due to low soil fertility, not growing desired crops according to the need of the soil, exhaustive over use of fertilizer. This low crop yield adversely affects the famers living condition. The low crop yield is the major reason for the plight of the farmers. The present work focuses on recommendation of suitable crops to be sown in the particular field or soil and also endorsing the farmer in sprinkling desired amount of fertilizer in the soil for the particular crop so that the farmer can make significant profit. Our work focuses on recommending four to five crops which are suitable for growing in particular type of field or soil either corresponding to the N, P, K, pH values entered by the farmer or district wise suitable crops. This system uses different parameters such as pH, N, P, K values to predict the suitable crops. Our work also facilitates endorsing farmer in sprinkling the required amount of fertilizer in their field for the particular crop by using the N, P, K values. The proposed system will give suggestion to the farmer to meet up the required deficient nutrient and minerals in the soil. This paper also contains about the various algorithms and their accuracy for recommending crops. Our work uses the Naïve Bayes Algorithm for recommending the suitable crops to the farmers. We have used this algorithm as it is highly efficient, easy to implement and works for high dimensional data. This algorithm is highly accurate in high dimensional space and data. The accuracy along with algorithm is well represented in a table. Our present work aims to address the low crop yield problem and offering farmers choices to grow one or more crops out of four to five crops suitable for the particular soil i.e. companion cropping so that they could make high profit and also maintains soil fertility.
Smart agriculture, also known as precision agriculture, is an innovative approach to farming that integrates technology and data analytics to optimize crop yields, reduce waste, and minimize environmental impact. Precision agriculture (PA) is an agricultural management concept based on monitoring, measuring and responding to inter-field and intra-field variability of crops. PA is also sometimes referred to as precision agriculture, satellite agriculture, on-demand agriculture, and site-specific crop management (SSCM). Precision agriculture uses information technology (IT) to ensure that crops and soil receive exactly what they need for optimal health and productivity. It also ensures profitability, sustainability and environmental protection. It takes into account aspects such as soil type, terrain, weather, plant growth and yields in crop management. Smart agriculture projects use a variety of cutting-edge technologies, including sensors, drones, and machine learning algorithms, to collect and analyze data about soil conditions, weather patterns, plant growth, and other key variables. These machines help to collect the data of various nutrient and mineral such as N, P, K , pH etc. along with their value of the agriculture land soil. By applying advanced analytics and automation, farmers can make more informed decisions about planting, fertilizing, watering, and harvesting crops, leading to increased efficiency and profitability. Overall, smart agriculture represents an exciting opportunity to transform the way we grow food and manage natural resources. By harnessing the power of technology and data, we can create a more sustainable, efficient, and equitable food system that benefits farmers, consumers, and the planet.
Key Words: Naïve Bayes Algorithm, Crop Yield, High Dimensional data, Crop Companion
1.1 Benefits of Smart Agriculture
1.INTRODUCTION
After the data is collected, predictive analytics software uses the collected data to provide guidance to farmers regarding crop rotation, optimal planting times, harvesting, and soil management. Agricultural control centers can integrate sensor data and imaging inputs with other data to enable farmers to identify fields that need
Food is a fuel that our body requires to carry day to day functions. Food directly relates to crop farming. Cultivations of crops encompasses many agricultural practices taken care by farmers. The economists failed to evolve a system fair to farmers. As farmers are not
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