HANA: A Performance-Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

• Published in: Journal of nanomaterials 2022, Vol.2022 (1)
• Main Authors: Revathy, G., Aurchana, P., Arieth, R. Madonna, Kavitha, N. S., Ramalingam, A., Ramaswamy, Kiran
• Format: Article
• Language: English
• Subjects: Agricultural production; Climate; Climate change; Crop yield; Datasets; Decision trees; Estimates; Global warming; Machine learning; Marketing; Model accuracy; Modelling; Nanomaterials; Neural networks; Optimization; Support vector machines; Tabu search
• ISSN: 1687-4110; 1687-4129
• DOI: 10.1155/2022/2658211

The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India’s soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real-time input of location-specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value.