spot or selective spraying using AI camera and machine learning models for detecting pest, weeds or diseases within Agricultural crop in fields,
this process should be cost-effective and maintain the min. level of chemical resides,
i think that high value crops, organic products crops, aromatic and herbicides crops might be the best options for this operation,
however, i am going to start plating a specific crop to start working on the Machine learning model for disease, weed or pest detection, so i need recommendations for the best crop according to the application.
Thinking about weed detection, it is better to use a easyly distinguishable crop in early stages, for example sunflower, soybean, rapeseed, although there is always a risk of confusion with weeds from the same family.
It would be more complicated to use a monocotyledonous crop, such as wheat, barley, rye, since it is difficult to differentiate them in the early stages with other monocotyledonous weeds, corn could be the exception.
Also a crop that is planted at a lower density per hectare, or with wider rows, would have an advantage over a high-density crop, for example a sunflower at 70,000 plants/ha better than a rapeseed at 400,000 plants/ha.
Javier Fuertes many thanks for your answer,
in Egypt there are many Ag. seasons, regions, crops and soil types which make it quite confused to select the best crop to start with, i do agree with the idea of focusing on less density planting crops and wider rows that may include some vegetable crops, potato, onion, peanuts and others which planted in rows with space range 45-90cm
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