Sorghum Plant Counting Using Patch-Wise CNN and Histogram Equalization

Due to the increasing impact of climate change on agriculture, efficient early-stage control and yield prediction are becoming increasingly crucial. While satellite data has demonstrated significant efficiency in certain applications, deep learning solutions for plant phenotyping and UAV-based imaging have emerged as promising alternatives. Early-stage plant counting is a key strategy for yield forecasting and mitigating the risk of insufficient production. Manual crop counting, however, is time-consuming, expensive, error-prone, and labor-intensive. Automated, accurate counting offers a significant reduction in workload. This study proposes an automating plant counting method using aerial images of sorghum crops. A histogram equalization is used at the image level then a patch-wise counting is derived from dot annotations of patches using a complexity-optimized regression-based deep convolutional neural network (DCNN). The proposed approach estimates the number of plants within patches and aggregates these estimates for each image. Our findings demonstrate that this approach outperforms existing image level regression-based solutions on the same dataset, achieving a lower Mean Absolute Error (MAE) and provides similar results to more complex architectures that rely on pointwise localization while requiring fewer parameters.