Yield Estimation of High-Density Cotton Fields Using Low-Altitude UAV Imaging and Deep Learning

Background: Different from other parts of the world, China has its own cotton planting pattern. Cotton are densely planted in wide-narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate evaluation of cotton yields using remote sensing in such field with branches occluded and overlapped. Results: In this study, low-altitude unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate the yields of densely planted cotton. Images of cotton field were acquired by an UAV at the height of 5 m. Cotton bolls were manually harvested and weighted afterwards. Then, a modified DCNN model was developed by applying encoder-decoder recombination and dilated convolution for pixel-wise cotton boll segmentation termed CD-SegNet. Linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Yield estimations of four cotton fields were verified after machine harvest and weighting. The results showed that CD-SegNet outperformed the other tested models including SegNet, support vector machine (SVM), and random forest (RF). The average error of the estimated yield of the cotton fields was 6.2%. Conclusions: Overall, the yield estimation of densely planted cotton based on lowaltitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

Applications of ANN for Agriculture Using Remote Sensed Data

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defense, intelligence, commerce, economics, and administrative planning. Remote sensing is used in science and technology, and through it, an object can be identified, measured, and analyzed without physical presence for interpretation. In India remote sensing has been using since 1970s. One among these applications is the crop classification and yield estimation. Using remote sensing in agriculture for crop mapping, and yield estimation provides efficient information, which is mainly used in many government organizations and the private sector. The pivotal sector for ensuring food security is a major concern of interest in these days. In time, availability of information on agricultural crops is vital for making well-versed decisions on food security issues.

Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study

Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.

YIELD ESTIMATION OF SUNFLOWER PLANT WITH CNN AND ANN USING SENTINEL-2

Due to food security and agricultural land management, it is crucial for decision makers and farmers to predict crop yields. In remote sensing based agricultural studies, spectral resolutions of satellite images, as well as temporal and spatial resolution, are important. In this study, we investigated whether there is a relationship between the Normalized Different Vegetation Index (NDVI) and Normalized Different Vegetation Index Red-edge (NDVIred) indices derived from the Sentinel-2 satellite. In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation. In this context, yield data of 48 sunflower parcels were obtained in 2018. The obtained results showed that both NDVI and NDVIred can be used to estimate the yield of sunflowers. The best results were obtained from the combination of the NDVI and the CNN technique with the RMSE equal to 20,874 Kg/da on 30 June 2018. Concerning the results, although there is not much superiority between the two indices, the best results were generally obtained from CNN as the method.