Search

The tomato (Solanum lycopersicum (Solanaceae)) is particularly susceptible to Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), a pest that directly and profoundly influences tomato yields. Consequently, the early detection of T. absoluta damage intensity on leaves using machine learning or artificial intelligence-based algorithms is crucial for effective pest control. In this ground-breaking study, the galleries generated by T. absoluta were examined via field images using the Decision Trees (DTs) algorithm, a machine learning method. The unique advantage of DTs over other algorithms is their inherent capacity to identify complex and vague shapes without the necessity of feature extraction, providing a more streamlined and effective approach. The DTs algorithm was meticulously trained using pixel values from the leaf images, leading to the classification of pixels within regions with and without galleries on the leaves. Accordingly, the gallery intensity was determined to be 9.09% and 35.77% in the test pictures. The performance of the DTs algorithm, as evidenced by a high precision and an accuracy rate of 0.98 and 0.99 respectively, testifies to its robust predictive and classification abilities. This pioneering study has far-reaching implications for the future of precision agriculture, potentially informing the development of advanced algorithms that can be integrated into autonomous vehicles. The integration of DTs in such applications, due to their unique ability to handle complex and indistinct shapes without the need for feature extraction, sets the stage for a new era of efficient and effective pest control strategies. [ABSTRACT FROM AUTHOR]

In recent years, apprehensions surrounding the pervasive employment of chemical control methods in global agricultural production have intensified, primarily due to their detrimental effects on non-target organisms. This situation accentuates the importance of technology-driven alternatives for managing plant diseases in agriculture. One such technological innovation, thermal imaging technology, has emerged as a promising tool for the early detection of plant diseases. Infections often induce stress in plants, leading to either elevated or reduced temperatures at the point of infection. It is postulated that thermal imaging may effectively identify such temperature deviations in plant tissues afflicted by disease during the initial stages. The study investigated temperature differences in leaves infected by Erysiphe cichoracearum, with disparities up to 1.6 °C. Over three weeks, the surface temperatures of numerous leaves were analysed at 30-minute intervals. In three weeks period, it was shown that infected leaf surfaces had significantly lower average daily temperatures than ambient and healthy leaf temperatures. Furthermore, healthy leaf temperatures remained consistently lower than ambient temperatures throughout the study. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the leading causes of yield losses in agricultural areas. In the fight against these diseases, chemical control methods are frequently used. However, this method of combat usually begins after the disease has spread throughout the entire field. The most essential thing here is to control the disease before it spreads throughout the entire country. Thermal imaging methods can now be used to accomplish this. Plant diseases stress the plant as a result of infection. The plant's stress causes activities that cause a temperature increase or reduction in the area where the infection has occurred or has begun. Thermal imaging technologies can be used to identify this condition. This work focuses on the potential early detection of Cucurbit powdery mildew (Sphaerotheca fuliginea (Schlech.) Polacci), which causes considerable yield loss in Cucurbitaceae, utilizing thermal imaging technologies. According to the findings, the lowest temperature in infected leaf tissues was 8.2 °C, whereas the maximum temperature in plant tissues without infection was 10.2 °C. The findings suggest that thermal imaging technology could be used to identify powdery mildew in cucurbits. In this case, early detection will potentially enable the detection of the disease that has started to spread in a certain region and will allow the disease to be potentially controlled with less labor and chemical use. [ABSTRACT FROM AUTHOR]