Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

中文翻译：

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用于检测有机和非有机棉花病害的机器学习算法的比较分析

棉花是生产的天然纤维，是在总农业用地 2.5% 的单一栽培中种植的经济作物。棉花是一种抗旱作物，可为在气候变化威胁的地区下种植的农民提供可靠的收入。这些棉花作物受到细菌、真菌、病毒和其他寄生虫病的影响，这些疾病可能因气候条件而异，导致作物生产力低下。最容易发生疾病的是叶子，它会导致植物受损，有时甚至是整个作物。大多数病害仅发生在棉花植株的叶子部分。病害检测的主要目的一直是使用传统技术在早期识别影响植物的病害，以提高生产效率。为了适当地检测这些棉叶病害，