Smart farming revolution: Leveraging machine learning for sustainable agriculture

Problem

In today's world, agriculture faces several challenges, including environmental degradation, crop diseases, and ineffective resource utilization. These challenges affect sustainability as well as food security.

Method

ology: In order to address the above-mentioned problems, several machine learning (ML) approaches, namely, Convolutional Neural Network (CNN), Random Forest (RF), Long Short Term Memory (LSTM), and other deep neural models, were used. These techniques aid in maximizing resource allocation, improving climate resilience, and enhancing early disease detection in agricultural ecosystems. Data for this study were collected through a questionnaire survey from 847 farmers across three main regions of China and were filtered using random sampling. Moreover, the collected data were analyzed through the Statistical Package for the Social Sciences (SPSS).

Results

The ML-based technique improved productivity, reduced operational costs, and enhanced sustainability. It attained a 20 % crop yield increase, minimized water consumption by 15 %, and reduced pesticide costs by $5000 per year in comparison to conventional farming practices.

Impact

This study shows the ability of ML technologies to revolutionize traditional agriculture, fostering stronger and sustainable agriculture systems that can sustain contemporary demands without compromising ecological and economic stability.