EARLY DETECTION OF CROP STRESS USING A SPATIO-TEMPORAL CNN–LSTM APPROACH

Reduced yield and prolonged crop stress are consequences of resource limits, climate change,soil erosion, which in turn complicate agricultural practices. Because they are reactive on data from only one source or field observations,traditional monitoring systems cannot handle problems head-on.Using information gathered from IoT devices and multispectral satellite images, this research presents a spatio-temporal deep learning system that can identify potential symptoms of crop stress in their early stages.The suggested approach uses convolution nural network and long short term memory networks to gtrack changes in environmental and agricultural variables  over time and to collectlocation based information from vegetation indices like the normalized difference vegetation index. Water shortages,dietary inadequacies and environmental changes may be more accurately detected with the use of multi-modal data fusion. The suggested model out performance independant techniques in experimental evolutions for prediction accuracy and resilience. Technology improves decision making via timely alrets,which reduces crop losses and increases resource efficiency. By advocating for environmental friendly farming methods and robust agricultural sysytems that can endure the effects of climate  chnage, the suggested model demonstrates its use in precision agriculture.