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，在不需要的植物物种窃取资源并对农民种植的农业经济作物造成损害之前将其消灭。对整个农田施用除草剂而不是对单个植物施用除草剂是一种浪费。然而，在大规模粮食生产中，单个杂草的识别和靶向还不可行。虽然已经取得了很大的进展，但这些技术仍然不够强大，无法准确区分杂草和作物，特别是在现实世界的条件下，在一块田里有多种杂草。因此，EngageGrant的目的是创建一个实时系统，对杂草和作物的数字图像进行分类，其目标是减少粮食生产中除草剂的使用量，最终提高作物产量并降低成本。该系统的重点是利用深度神经网络对玉米、油菜籽以及杂草进行分类，包括加拿大蓟、黄花菜、重根藜和蒲公英。其好处包括减少加拿大农民的环境足迹，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.