In the agricultural industry, plant diseases and pests pose the greatest risks. Lime is rich 10 source of vitamin C which works as an immunity booster in human body. Because of the late and manually diseases detection in lime causes a vast loss in crop production worldwide. The most common diseases are found in limes are lime canker, lemon scab, brown rot, sooty mould and Armillaria. In this paper we used imaging and non-imaging (spectral based sensing) methods with the combination of machine learning technique to detect the lime canker and sooty mould diseases. Image acquirement, pre-processing, segmentation and classification are all steps in the imaging methodology, which is then followed by feature extraction. In non-imaging methodology a multispectral sensor (Spectrometer) is used with 400 nm to 1000 nm wavelength to detect the diseases. training set and test set ratio is fixed for both techniques are 75% and 25% respectively. When it comes to identifying and classifying lime disease, spectroscopy has a 99% efficiency rating compared to imaging methodology's 96%.

This review paper is for quality control of rice which is most important crop for human as well as in food market using image processing techniques and computer vision. Basic problem in Indian food industry that performs quality check manually by human inspectors which is non-reliable, costly and time consuming.

Nowadays, IoT is impacting day-to-day lifestyles by making everything smart. Agriculture is the backbone of the economy of every country. In the past, farmers used to examine soil, weather, and crops using traditional methods, but with the smart innovative sensors and the help of IoT, this type of work can be easily done without any human efforts. The IoT-based smart agriculture system can be operated from anywhere by using networking technologies. This paper presents a survey of IoT technologies and applications in the agriculture sector. The survey article presents different research works in this domain, including applications, tools, and technologies, as well as approaches implemented in the field of agriculture using IoT. Furthermore, it discusses the major obstacles and challenges faced in the field of agriculture. Lastly, open challenges have been presented to assist researchers with possible solutions.