A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

Abstract

<p>India is an agricultural country. Agriculture is the important contributor to the Indian economy. There are many classification techniques like Support Vector Machine(SVM), LADTree, Natve Bayes, Bayesnet, K Nearest Neighbour(KNN), Locally Weighted Learning(LWL) on rice crop production datasets. They have some drawbacks like low accuracy and more errors. To achieve more significant result, To increase classification accuracy and reducing classification errors, our research uses classification method Bayesnet based adaboost will be proposed in work. Rice crop yield depend on environment's parameters like Rainfall, minimum temperature, average temperature, Maximum temperature, Vapour Pressure, potential evapotranspiration, reference crop evapotranspiration, cloud cover, wet day frequency for the kharif season. our dataset containing these environmental parameters for accurate prediction of Rice crop yield.</p>

A Comprehensive Analysis of Machine Learning-Based Assessment and Prediction of Soil Enzyme Activity

“…The soil enzyme activity-such as urease, invertase, C-glucosidase, and acid phosphatase, which are associated with low fertility levels-is estimated. Based on this prediction, cucumber, maize, peanut pepper, soybean, and sugarcane were identified as crops that are useful for harvesting and increasing crop productivity [33][34][35]. Based on the analysis of soil fertility level and soil enzyme activity using machine learning algorithms, specific crops are suggested for increasing crop productivity.…”