Purpose of Review Weed detection systems are important solutions to one of the existing agricultural problems-unmechanized weed control. Weed detection also helps provide a means of reducing or eliminating herbicide use, mitigating agricultural environmental and health impact, and improving sustainability. Recent Findings Deep learning-based techniques are replacing traditional machine learning techniques to detect weeds in real time with the development of new models and increasing computational power. More hybrid machine learning models are emerging, utilizing benefits from different techniques. More large-scale crop and weed image datasets are available online now, and this provides more data and opportunities for researchers and engineers to join and contribute to this field. Summary This article provides a mini-review of all the different emerging and popular weed detection techniques for selective spraying, and summarizes the trends in this area in the past several years.

Silicon is found in all plants and the accumulation of silicon can improve plant tolerance to biotic stress. Strawberry powdery mildew (Podosphaera aphanis) and two-spotted spider mite (Tetranychus urticae) are both detrimental to strawberry production worldwide. Two field trials were done on a UK commercial strawberry farm in 2014 and 2015, to assess the effects of silicon nutrient applied via the fertigation system on P. aphanis and T. urticae. The silicon treatments decreased the severity of both P. aphanis and T. urticae in two consecutive years on different cultivars. The percentage leaf area infected with P. aphanis mycelium from silicon treated plants were 2.19 (in 2014) and 0.41 (in 2015) compared with 3.08 (in 2014) and 0.57 (in 2015) from the untreated plants. The etiology of the pathogen as measured by the Area Under the Disease Progress Curve from silicon (with and without fungicides) treatments was 152.7 compared with 217.5 from non-silicon (with and without fungicides) treatments for the overall period of 2014–2015. The average numbers of T. urticae recorded on strawberry leaves were 1.43 (in 2014) and 1.83 (in 2015) in plants treated with silicon compared with 8.82 (in 2014) and 6.69 (in 2015) in untreated plants. The silicon contents of the leaves from the silicon alone treatment were 26.8 μg mg-1 (in 2014) and 22.2 μg mg-1 (in 2015) compared with 19.7 μg mg-1 (in 2014) and 21.4 μg mg-1 (in 2015) from the untreated. The silicon nutrient root application contributed to improved plant resilience against P. aphanis and T. urticae. Silicon could play an important role in broad spectrum control of pests and diseases in commercial strawberry production.

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Strawberry powdery mildew (Podosphaera aphanis) causes serious losses in UK crops, potentially reducing yields by as much as 70%. Consequently, conventional fungicide application programmes tend to recommend a prophylactic approach using insurance sprays, risking the development of fungicide insensitivity and requiring careful management relative to harvest periods to avoid residual fungicides on harvested fruit. This paper describes the development of a prediction system to guide the control of P. aphanis  by the application of fungicides only when pathogen infection and disease progression are likely. The system was developed over a 15-year period on commercial farms starting with its establishment, validation and then deployment to strawberry growers. This involved three stages: 1. Identification and validation of parameters for inclusion in the prediction system (2004-2008). 2. Development of the prediction system in compact disc format (2009-2015). 3. Development and validation of the prediction system in a web-based format and cost-benefit analysis (2016-2020). The prediction system was based on the temporal accumulation of conditions (temperature and relative humidity) conducive to the development of P. aphanis , which sporulates at 144 accumulated disease-conducive hours. Sensitivity analysis was performed to refine the prediction system parameters. Field validation of the results demonstrated that to effectively control disease, the application of fungicides was best done between 125 and 144 accumulated hours of disease-conducive conditions. A cost-benefit analysis indicated that, by comparison with the number and timing of fungicide applications in conventional insurance disease control programmes, the prediction system enabled good disease control with significantly fewer fungicide applications (between one and four sprays less) (df=7, t=7.6, p =0.001) and reduced costs (savings between £35-£493/hectare) (df=7, t=4.0, p =0.01) for the growers.