In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear Regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps the farmers in preserving their time by assisting them in the decision-making process.
PurposeAutomated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop previously. They were not always able to predict the crop and its yield solely on that idea alone. Second, seed firms frequently monitor how well new plant varieties would grow in certain settings. Third, predicting agricultural production is critical for solving emerging food security concerns, especially in the face of global climate change. Accurate production forecasts not only assist farmers in making informed economic and management decisions but they also aid in the prevention of famine. This results in farming systems’ efficiency and productivity gains, as well as reduced risk from environmental factors.Design/methodology/approachThis research paper proposes a machine learning technique for effective autonomous crop and yield prediction, which makes use of solution encoding to create solutions randomly, and then for every generated solution, fitness is evaluated to meet highest accuracy. Major focus of the proposed work is to optimize the weight parameter in the input data. The algorithm continues until the optimal agent or optimal weight is selected, which contributes to maximum accuracy in automated crop prediction.FindingsPerformance of the proposed work is compared with different existing algorithms, such as Random Forest, support vector machine (SVM) and artificial neural network (ANN). The proposed method support vector neural network (SVNN) with gravitational search agent (GSA) is analysed based on different performance metrics, such as accuracy, sensitivity, specificity, CPU memory usage and training time, and maximum performance is determined.Research limitations/implicationsRather than real-time data collected by Internet of Things (IoT) devices, this research focuses solely on historical data; the proposed work does not impose IoT-based smart farming, which enhances the overall agriculture system by monitoring the field in real time. The present study only predicts the sort of crop to sow not crop production.Originality/valueThe paper proposes a novel optimization algorithm, which is based on the law of gravity and mass interactions. The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles. A comparison was made between the suggested method and various existing strategies. The obtained results confirm the high-performance in solving diverse nonlinear functions.
This paper describes the development of an economical, convenient, and reliable GSM based automated fire alarm system for remotely alerting fire and smoke incidents to the property owner outside the building or industrial premises quickly by sending short message via GSM network. Along with study of existing fire detecting systems. The proposed system uses N-F-N Gateway which is an intelligent gateway interface for fire monitoring workstation. It also supports full panel programming using embedded c and network diagnostics. General TermsThis paper is an embedded based, embedded means dumping of software into the hardware. Here, software code is written using Embedded-C. This work makes use of Q26 series GSM based development kit and is used to allow users to create and define applications using the Q2686 embedded module. GSM refers to second-generation wireless telecommunications standard for digital cellular services.
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