Abstract
In an agricultural country like India, planning and execution play an essential role in upsurging productivity with minimal cost. Crop production is based on different parameters like soil, moisture, temperature, rainfall, seeds, seasons, and crop production planning. Various parameters play crucial roles in crop production and in improving farmer economic situations. The data mining concept has come into the picture for a better understanding of the crop market. In this chapter, different machine learning methods are being analyzed to showcase the application and importance of applying machine learning tools in the improvement of crop production. The research continues with the requirement of kinds of fertilizers and pesticides, types of crops produced as per the soil type, and Rabi and Kharif crops distinction for different seasons. The chapter critically examines the machine learning techniques for crop production. The research will provide a framework for researchers in the agricultural and technology sectors to understand crop production.
TopIntroduction
Agriculture is a critical sector in India, providing livelihoods to a substantial portion of the population. While the sector faces various challenges, it continues to be a backbone of the Indian economy and plays a pivotal role in ensuring food security for the nation. The cultivating and farming area give significant establishments to the Indian economy. Disregarding the way that India has accomplished self-maintainability in food staples its rural efficiency is low. Efforts to modernize farming practices, improve infrastructure, and enhance the well-being of farmers remain ongoing priorities in the agricultural sector in Indian economy (Wagh, 2016; Bais & Bahadur, 2023). The cultivating and farming area give significant establishments to the Indian economy. Disregarding the way that India has accomplished self-maintainability in food staples its rural efficiency is low. India because of its changed actual elements has assortment of soil which is extraordinarily ideal for developing various harvests and it is powerful country for rice creation everywhere. To obtain wanted result farmers should definitively know when to plant the seeds. Also, India encounters shifted precipitation at better places which straightforwardly influences the harvest on the grounds that both low and extreme precipitation will make harm crops; thus, genuine precipitation is wanted for better yield creation. Many factors, for example, temperature, humidity likewise influence the harvest yield. Farming results depend upon geographical, climatic circumstances and season. The horticultural yield gives one of the quantifiable boundaries that contribute towards the real pay of the country. This is still up in the air based on developed regions and harvest creation of whole country. To get further developed monetary results we ought to concentrate on the agrarian information base comprising information about crops, season, region, creation, and so forth. Study and examination of such enormous rural data set is brought out using different information mining strategies. Information mining is the most common way of dissecting enormous informational indexes to decide helpful examples and removing significant data, this cycle is otherwise called information extraction. Information extraction from enormous informational index is finished by applying different information mining procedures. There are number of public area associations which comprise of exhaustive hotspot for farming information, either for instructive reasons or different practices connected with horticulture. This information is generally accessible on Indian sites. Many open-source devices for mining information are accessible to decide patterns and expectations connected with agrarian results and harvest yield. Today, Data mining plays a significant role in agriculture by helping farmers, researchers, and agricultural organizations make informed decisions, optimize processes, and enhance overall productivity which ultimately improve the farmer’s economical condition which is the most concerned goal of agriculturally based economy (Raorane & Kulkarni, 2013; Macuácua et al., 2023; Medar & Rajpurohit, 2014; Li et al., 2023; Paul et al., 2015; Hira & Deshpande, 2015). Here are some ways data mining is used in agriculture:
Key Terms in this Chapter
Data Mining: Data mining uncovers valuable insights for informed soil classification and crop choices. It identifies trends, such as soil characteristics' impact on crop yield.
Soil Data Analysis: Soil data analysis provides crucial insights for effective soil and crop management. Machine learning interprets soil data, supporting data-driven farming decisions.
Agricultural Production: Data-driven strategies increase yields and reduce waste in agricultural production. Precision agriculture transforms farming into an efficient, sustainable practice.
Soil Classification: Soil classification aids crop selection by understanding unique soil properties. Machine learning identifies suitable crops for different soil types.
Crop Prediction: Machine learning aids in accurate crop prediction for better harvest planning. Data mining reveals hidden insights for precise crop yield forecasts.
Precision Agriculture: Precision agriculture optimizes farming with technology and data for efficiency and sustainability. Resource use is fine-tuned, reducing environmental impact and boosting productivity.
Machine Learning: Machine learning optimizes tasks like irrigation and pest control in agriculture. Real-time decisions benefit agricultural production through adaptable machine learning models.
Crop Selection Optimization: Optimization tools help farmers choose the best crops for specific soil and climate conditions. This leads to increased yields and improved financial outcomes.