Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.

The recent disciplinal progresses of agricultural entomology in China

In this paper, four recent advances and achievements of China in agricultural insect research, namely, on the genome of silkworm (Bombyx mori Linnaeus), on the geographical differentiation and regional migration of cotton bollworm (Helicoverpa armigera (Hübner)), on the standardized monitoring techniques for safety of honey bee (Apis mellifera Linnaeus) products, and on the virus transmission property of small brown planthopper (Laodelphax striatellus (Fallén)) as well as the interactions between vector and rice stripe virus (RSV), were reported. All of these researches are very important for controlling agricultural insect pests and the diseases they transmit, accelerating the molecular biological research of silkworm, and promoting the international trade of honey bee products. Most of these achievements mentioned above have got the national, provincial, ministerial or municipal awards on science and technology.

Advanced agricultural disease image recognition technologies: A review

Agricultural disease image recognition has an important role to play in the field of intelli-gent agriculture.Some advanced machine learning methods associated with the develop-ment of artificial intelligence technology in recent years,such as deep learning and transfer learning,have begun to be used for the recognition of agricultural diseases.However,the adoption of these methods continues to face a number of important challenges.This paper looks specifically at deep learning and transfer learning and discusses the recent progress in the use of these advanced technologies for agricultural disease image recognition.Anal-ysis and comparison of these two methods reveals that current agricultural disease data resources make transfer learning the better option.The paper then examines the core issues that require further study for research in this domain to continue to progress,such as the construction of image datasets,the selection of big data auxiliary domains and the optimization of the transfer learning method.Creating image datasets obtained under actual cultivation conditions is found to be especially important for the development of practically viable agricultural disease image recognition systems.