Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…
Deep Hybrid Neural Networks on Multi-temporal Satellite Data: Predicting Farm-scale Crop Yields
Master's thesis in Information- and communication technology (IKT590) Accurate farm-scale crop yield predictions can enable farmers to improve their yield per decare and inform subsequent sectors of the availability of grains sooner. Existing research on yield predictions is limited to regional analytics, which often fails to capture local yield variations influenced by farm management decisions and field conditions. Farm-scale crop yield predictions require precise ground-truth prediction targets, which are not always available. It takes substantial manual labor to create large and suitable datasets of high-resolution per-farm samples. This thesis introduces a hybrid multi-temporal deep ne…