Real-time, machine-learning weed detection system for autonomous agricultural machines

目前的农业做法包括全面使用除草剂，以消除不需要的植物物种，防止它们窃取资源并对农民种植的经济作物造成损害。把除草剂用在整块田地上，而不是单独用在植物上，是浪费的。然而，在大规模粮食生产中识别和瞄准个体杂草是不可行的。虽然已经取得了很大进展，但这些技术仍然不够强大，不足以准确区分杂草和农作物，特别是在现实世界中，一块田地里有多种杂草。因此，EngageGrant的目标是创建一个实时系统来对杂草和农作物的数字图像进行分类，目标是减少在粮食生产中使用的除草剂的数量，最终导致作物产量的增加和成本的降低。拟议的系统的重点是使用深度神经网络对玉米和油菜籽以及杂草加拿大草、苍耳、猪草和蒲公英进行分类。好处包括减少加拿大农民的环境足迹，以及降低食品生产成本。

Current agricultural practices include blanket application of herbicides for eliminating unwanted plant speciesbefore they can steal resources and cause harm to agricultural cash crops cultivated by farmers. Applyingherbicides to entire fields instead of individual plants is wasteful. However, identifying and targeting individualweeds has not been feasible in large-scale food production. While much progress has been made, thesetechniques are still not robust enough to accurately differentiate between weeds and crops, especially inreal-world conditions consisting of multiple weed species within a single field. Thus, the aim of this EngageGrant is to create a real-time system to classify digital images of weeds and crops, where the goal is reducingthe amount of herbicide used in food production, ultimately leading to increased crop yields and lower costs.The focus of the proposed system is to classify the crop species of corn and canola as well as the weeds Canadathistle, cocklebur, redroot pigweed, and dandelion using deep neural networks. The benefits includeenvironmental footprint reduction for Canadian farmers, and lower food production costs.