Artificial intelligence applications enable farmers to optimize crop growth
and production while reducing costs and environmental impact. Computer
vision-based algorithms in particular, are commonly used for fruit
segmentation, enabling in-depth analysis of the harvest quality and accurate
yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based
model for semantic segmentation of on-plant tomatoes. When evaluated against
other competitive methods, our model demonstrates state-of-the-art (SOTA)
performance, even in challenging environments with highly occluded fruits.
Additionally, we introduce Tomatopia, a new, large and challenging dataset of
greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and
pixel-level annotations of the fruits.

本研究提出了 TomatoDIFF，一种新的基于扩散的模型，用于对植物上的西红柿进行语义分割。在高度遮挡的环境中，我们的模型表现出卓越的性能，甚至超过其他竞争方法。此外，我们还介绍了一个新的、大规模且具有挑战性的数据集 Tomatopia，其中包括高分辨率 RGB-D 图像和水果的像素级注释。

使用去噪扩散模型实现番茄植株分割

TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models

Deep reinforcement learning has considerable potential to improve irrigation
scheduling in many cropping systems by applying adaptive amounts of water based
on various measurements over time. The goal is to discover an intelligent
decision rule that processes information available to growers and prescribes
sensible irrigation amounts for the time steps considered. Due to the technical
novelty, however, the research on the technique remains sparse and impractical.
To accelerate the progress, the paper proposes a general framework and
actionable procedure that allow researchers to formulate their own optimisation
problems and implement solution algorithms based on deep reinforcement
learning. The effectiveness of the framework was demonstrated using a case
study of irrigated wheat grown in a productive region of Australia where
profits were maximised. Specifically, the decision rule takes nine state
variable inputs: crop phenological stage, leaf area index, extractable soil
water for each of the five top layers, cumulative rainfall and cumulative
irrigation. It returns a probabilistic prescription over five candidate
irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system
was simulated at Goondiwindi using the APSIM-Wheat crop model. After training
in the learning environment using 1981--2010 weather data, the learned decision
rule was tested individually for each year of 2011--2020. The results were
compared against the benchmark profits obtained using irrigation schedules
optimised individually for each of the considered years. The discovered
decision rule prescribed daily irrigation amounts that achieved more than 96%
of the benchmark profits. The framework is general and applicable to a wide
range of cropping systems with realistic optimisation problems.

该研究采用深度强化学习技术，提出了一种有效的灌溉方法，旨在为农民提高粮食产量和效益。对于考虑的每天，决策规则基于九个状态变量输入，返回每天五个不同灌溉量的概率性建议，经过测试证明该决策规则可以在大多数情况下同时实现最高的灌溉效益和经济效益。